UPDATE: Adds 56 functions in the Statistical section

Uses statrs for numerical functions

REFACTOR: Put statistical functions on its own module

This might seem counter-intuitive but the wasm build after this refactor
is 1528 bytes smaller :)

base/src/expressions/parser/static_analysis.rs

@@ -711,6 +711,7 @@ fn get_function_args_signature(kind: &Function, arg_count: usize) -> Vec<Signatu
         Function::Value => args_signature_scalars(arg_count, 1, 0),
         Function::Valuetotext => args_signature_scalars(arg_count, 1, 1),
         Function::Average => vec![Signature::Vector; arg_count],
+        Function::Avedev => vec![Signature::Vector; arg_count],
         Function::Averagea => vec![Signature::Vector; arg_count],
         Function::Averageif => args_signature_sumif(arg_count),
         Function::Averageifs => vec![Signature::Vector; arg_count],
@@ -889,6 +890,105 @@ fn get_function_args_signature(kind: &Function, arg_count: usize) -> Vec<Signatu
         Function::Dvar => vec![Signature::Vector, Signature::Scalar, Signature::Vector],
         Function::Dvarp => vec![Signature::Vector, Signature::Scalar, Signature::Vector],
         Function::Dstdevp => vec![Signature::Vector, Signature::Scalar, Signature::Vector],
+
+        Function::BetaDist => args_signature_scalars(arg_count, 4, 2),
+        Function::BetaInv => args_signature_scalars(arg_count, 3, 2),
+        Function::BinomDist => args_signature_scalars(arg_count, 4, 0),
+        Function::BinomDistRange => args_signature_scalars(arg_count, 3, 1),
+        Function::BinomInv => args_signature_scalars(arg_count, 3, 0),
+        Function::ChisqDist => args_signature_scalars(arg_count, 4, 0),
+        Function::ChisqDistRT => args_signature_scalars(arg_count, 3, 0),
+        Function::ChisqInv => args_signature_scalars(arg_count, 3, 0),
+        Function::ChisqInvRT => args_signature_scalars(arg_count, 2, 0),
+        Function::ChisqTest => {
+            if arg_count == 2 {
+                vec![Signature::Vector, Signature::Vector]
+            } else {
+                vec![Signature::Error; arg_count]
+            }
+        }
+        Function::ConfidenceNorm => args_signature_scalars(arg_count, 3, 0),
+        Function::ConfidenceT => args_signature_scalars(arg_count, 3, 0),
+        Function::CovarianceP => {
+            if arg_count == 2 {
+                vec![Signature::Vector, Signature::Vector]
+            } else {
+                vec![Signature::Error; arg_count]
+            }
+        }
+        Function::CovarianceS => {
+            if arg_count == 2 {
+                vec![Signature::Vector, Signature::Vector]
+            } else {
+                vec![Signature::Error; arg_count]
+            }
+        }
+        Function::Devsq => vec![Signature::Vector; arg_count],
+        Function::ExponDist => args_signature_scalars(arg_count, 3, 0),
+        Function::FDist => args_signature_scalars(arg_count, 4, 0),
+        Function::FDistRT => args_signature_scalars(arg_count, 3, 0),
+        Function::FInv => args_signature_scalars(arg_count, 3, 0),
+        Function::FInvRT => args_signature_scalars(arg_count, 3, 0),
+        Function::Fisher => args_signature_scalars(arg_count, 1, 0),
+        Function::FisherInv => args_signature_scalars(arg_count, 1, 0),
+        Function::Gamma => args_signature_scalars(arg_count, 1, 0),
+        Function::GammaDist => args_signature_scalars(arg_count, 4, 0),
+        Function::GammaInv => args_signature_scalars(arg_count, 3, 0),
+        Function::GammaLn => args_signature_scalars(arg_count, 1, 0),
+        Function::GammaLnPrecise => args_signature_scalars(arg_count, 1, 0),
+        Function::HypGeomDist => args_signature_scalars(arg_count, 5, 0),
+        Function::LogNormDist => args_signature_scalars(arg_count, 4, 0),
+        Function::LogNormInv => args_signature_scalars(arg_count, 3, 0),
+        Function::NegbinomDist => args_signature_scalars(arg_count, 4, 0),
+        Function::NormDist => args_signature_scalars(arg_count, 4, 0),
+        Function::NormInv => args_signature_scalars(arg_count, 3, 0),
+        Function::NormSdist => args_signature_scalars(arg_count, 2, 0),
+        Function::NormSInv => args_signature_scalars(arg_count, 1, 0),
+        Function::Pearson => {
+            if arg_count == 2 {
+                vec![Signature::Vector, Signature::Vector]
+            } else {
+                vec![Signature::Error; arg_count]
+            }
+        }
+        Function::Phi => args_signature_scalars(arg_count, 1, 0),
+        Function::PoissonDist => args_signature_scalars(arg_count, 3, 0),
+        Function::Standardize => args_signature_scalars(arg_count, 3, 0),
+        Function::StDevP => vec![Signature::Vector; arg_count],
+        Function::StDevS => vec![Signature::Vector; arg_count],
+        Function::Stdeva => vec![Signature::Vector; arg_count],
+        Function::Stdevpa => vec![Signature::Vector; arg_count],
+        Function::TDist => args_signature_scalars(arg_count, 3, 0),
+        Function::TDist2T => args_signature_scalars(arg_count, 2, 0),
+        Function::TDistRT => args_signature_scalars(arg_count, 2, 0),
+        Function::TInv => args_signature_scalars(arg_count, 2, 0),
+        Function::TInv2T => args_signature_scalars(arg_count, 2, 0),
+        Function::TTest => {
+            if arg_count == 4 {
+                vec![
+                    Signature::Vector,
+                    Signature::Vector,
+                    Signature::Scalar,
+                    Signature::Scalar,
+                ]
+            } else {
+                vec![Signature::Error; arg_count]
+            }
+        }
+        Function::VarP => vec![Signature::Vector; arg_count],
+        Function::VarS => vec![Signature::Vector; arg_count],
+        Function::VarpA => vec![Signature::Vector; arg_count],
+        Function::VarA => vec![Signature::Vector; arg_count],
+        Function::WeibullDist => args_signature_scalars(arg_count, 4, 0),
+        Function::ZTest => {
+            if arg_count == 2 {
+                vec![Signature::Vector, Signature::Scalar]
+            } else if arg_count == 3 {
+                vec![Signature::Vector, Signature::Scalar, Signature::Scalar]
+            } else {
+                vec![Signature::Error; arg_count]
+            }
+        }
     }
 }
 
@@ -990,6 +1090,7 @@ fn static_analysis_on_function(kind: &Function, args: &[Node]) -> StaticResult {
         Function::Valuetotext => not_implemented(args),
         Function::Average => not_implemented(args),
         Function::Averagea => not_implemented(args),
+        Function::Avedev => not_implemented(args),
         Function::Averageif => not_implemented(args),
         Function::Averageifs => not_implemented(args),
         Function::Count => not_implemented(args),
@@ -1165,5 +1266,61 @@ fn static_analysis_on_function(kind: &Function, args: &[Node]) -> StaticResult {
         Function::Dvar => not_implemented(args),
         Function::Dvarp => not_implemented(args),
         Function::Dstdevp => not_implemented(args),
+
+        Function::BetaDist => StaticResult::Scalar,
+        Function::BetaInv => StaticResult::Scalar,
+        Function::BinomDist => StaticResult::Scalar,
+        Function::BinomDistRange => StaticResult::Scalar,
+        Function::BinomInv => StaticResult::Scalar,
+        Function::ChisqDist => StaticResult::Scalar,
+        Function::ChisqDistRT => StaticResult::Scalar,
+        Function::ChisqInv => StaticResult::Scalar,
+        Function::ChisqInvRT => StaticResult::Scalar,
+        Function::ChisqTest => StaticResult::Scalar,
+        Function::ConfidenceNorm => StaticResult::Scalar,
+        Function::ConfidenceT => StaticResult::Scalar,
+        Function::CovarianceP => StaticResult::Scalar,
+        Function::CovarianceS => StaticResult::Scalar,
+        Function::Devsq => StaticResult::Scalar,
+        Function::ExponDist => StaticResult::Scalar,
+        Function::FDist => StaticResult::Scalar,
+        Function::FDistRT => StaticResult::Scalar,
+        Function::FInv => StaticResult::Scalar,
+        Function::FInvRT => StaticResult::Scalar,
+        Function::Fisher => StaticResult::Scalar,
+        Function::FisherInv => StaticResult::Scalar,
+        Function::Gamma => StaticResult::Scalar,
+        Function::GammaDist => StaticResult::Scalar,
+        Function::GammaInv => StaticResult::Scalar,
+        Function::GammaLn => StaticResult::Scalar,
+        Function::GammaLnPrecise => StaticResult::Scalar,
+        Function::HypGeomDist => StaticResult::Scalar,
+        Function::LogNormDist => StaticResult::Scalar,
+        Function::LogNormInv => StaticResult::Scalar,
+        Function::NegbinomDist => StaticResult::Scalar,
+        Function::NormDist => StaticResult::Scalar,
+        Function::NormInv => StaticResult::Scalar,
+        Function::NormSdist => StaticResult::Scalar,
+        Function::NormSInv => StaticResult::Scalar,
+        Function::Pearson => StaticResult::Scalar,
+        Function::Phi => StaticResult::Scalar,
+        Function::PoissonDist => StaticResult::Scalar,
+        Function::Standardize => StaticResult::Scalar,
+        Function::StDevP => StaticResult::Scalar,
+        Function::StDevS => StaticResult::Scalar,
+        Function::Stdeva => StaticResult::Scalar,
+        Function::Stdevpa => StaticResult::Scalar,
+        Function::TDist => StaticResult::Scalar,
+        Function::TDist2T => StaticResult::Scalar,
+        Function::TDistRT => StaticResult::Scalar,
+        Function::TInv => StaticResult::Scalar,
+        Function::TInv2T => StaticResult::Scalar,
+        Function::TTest => StaticResult::Scalar,
+        Function::VarP => StaticResult::Scalar,
+        Function::VarS => StaticResult::Scalar,
+        Function::VarpA => StaticResult::Scalar,
+        Function::VarA => StaticResult::Scalar,
+        Function::WeibullDist => StaticResult::Scalar,
+        Function::ZTest => StaticResult::Scalar,
     }
 }

base/src/functions/mod.rs

@@ -190,6 +190,98 @@ pub enum Function {
     Minifs,
     Geomean,
 
+    Avedev,
+    BetaDist,
+    BetaInv,
+    BinomDist,
+    BinomDistRange,
+    BinomInv,
+    ChisqDist,
+    ChisqDistRT,
+    ChisqInv,
+    ChisqInvRT,
+    ChisqTest,
+    ConfidenceNorm,
+    ConfidenceT,
+    // Correl,
+    CovarianceP,
+    CovarianceS,
+    Devsq,
+    ExponDist,
+    FDist,
+    FDistRT,
+    FInv,
+    FInvRT,
+    // FTest,
+    Fisher,
+    FisherInv,
+    // Forecast,
+    Gamma,
+    GammaDist,
+    GammaInv,
+    GammaLn,
+    GammaLnPrecise,
+    // Gauss,
+    // Growth,
+    // Harmean,
+    HypGeomDist,
+    // Intercept,
+    // Kurt,
+    // Large,
+    // Linest,
+    // Logest,
+    LogNormDist,
+    LogNormInv,
+    // MaxA,
+    // Median,
+    // MinA,
+    // ModeMult,
+    // ModeSingl,
+    NegbinomDist,
+    NormDist,
+    NormInv,
+    NormSdist,
+    NormSInv,
+    Pearson,
+    // PercentileExc,
+    // PercentileInc,
+    // PercentrankExc,
+    // PercentrankInc,
+    // Permut,
+    // Permutationa,
+    Phi,
+    PoissonDist,
+    // Prob,
+    // QuartileExc,
+    // QuartileInc,
+    // RankAvg,
+    // RankEq,
+    // Rsq
+    // Skew,
+    // SkewP,
+    // Slope,
+    // Small,
+    Standardize,
+    StDevP,
+    StDevS,
+    Stdeva,
+    Stdevpa,
+    // Steyx,
+    TDist,
+    TDist2T,
+    TDistRT,
+    TInv,
+    TInv2T,
+    TTest,
+    // Trend,
+    // Trimmean,
+    VarP,
+    VarS,
+    VarpA,
+    VarA,
+    WeibullDist,
+    ZTest,
+
     // Date and time
     Date,
     Datedif,
@@ -328,7 +420,7 @@ pub enum Function {
 }
 
 impl Function {
-    pub fn into_iter() -> IntoIter<Function, 268> {
+    pub fn into_iter() -> IntoIter<Function, 324> {
         [
             Function::And,
             Function::False,
@@ -453,6 +545,7 @@ impl Function {
             Function::Type,
             Function::Sheet,
             Function::Average,
+            Function::Avedev,
             Function::Averagea,
             Function::Averageif,
             Function::Averageifs,
@@ -598,6 +691,61 @@ impl Function {
             Function::Dvar,
             Function::Dvarp,
             Function::Dstdevp,
+            Function::BetaDist,
+            Function::BetaInv,
+            Function::BinomDist,
+            Function::BinomDistRange,
+            Function::BinomInv,
+            Function::ChisqDist,
+            Function::ChisqDistRT,
+            Function::ChisqInv,
+            Function::ChisqInvRT,
+            Function::ChisqTest,
+            Function::ConfidenceNorm,
+            Function::ConfidenceT,
+            Function::CovarianceP,
+            Function::CovarianceS,
+            Function::Devsq,
+            Function::ExponDist,
+            Function::FDist,
+            Function::FDistRT,
+            Function::FInv,
+            Function::FInvRT,
+            Function::Fisher,
+            Function::FisherInv,
+            Function::Gamma,
+            Function::GammaDist,
+            Function::GammaInv,
+            Function::GammaLn,
+            Function::GammaLnPrecise,
+            Function::HypGeomDist,
+            Function::LogNormDist,
+            Function::LogNormInv,
+            Function::NegbinomDist,
+            Function::NormDist,
+            Function::NormInv,
+            Function::NormSdist,
+            Function::NormSInv,
+            Function::Pearson,
+            Function::Phi,
+            Function::PoissonDist,
+            Function::Standardize,
+            Function::StDevP,
+            Function::StDevS,
+            Function::Stdeva,
+            Function::Stdevpa,
+            Function::TDist,
+            Function::TDist2T,
+            Function::TDistRT,
+            Function::TInv,
+            Function::TInv2T,
+            Function::TTest,
+            Function::VarP,
+            Function::VarS,
+            Function::VarpA,
+            Function::VarA,
+            Function::WeibullDist,
+            Function::ZTest,
         ]
         .into_iter()
     }
@@ -659,6 +807,12 @@ impl Function {
             Function::Sec => "_xlfn.SEC".to_string(),
             Function::Sech => "_xlfn.SECH".to_string(),
             Function::Acot => "_xlfn.ACOT".to_string(),
+            Function::GammaLnPrecise => "_xlfn.GAMMALN.PRECISE".to_string(),
+            Function::Gamma => "_xlfn.GAMMA".to_string(),
+            Function::GammaInv => "_xlfn.GAMMA.INV".to_string(),
+            Function::GammaLn => "_xlfn.GAMMALN".to_string(),
+            Function::BetaDist => "_xlfn.BETA.DIST".to_string(),
+            Function::BetaInv => "_xlfn.BETA.INV".to_string(),
 
             _ => self.to_string(),
         }
@@ -811,6 +965,7 @@ impl Function {
 
             "AVERAGE" => Some(Function::Average),
             "AVERAGEA" => Some(Function::Averagea),
+            "AVEDEV" => Some(Function::Avedev),
             "AVERAGEIF" => Some(Function::Averageif),
             "AVERAGEIFS" => Some(Function::Averageifs),
             "COUNT" => Some(Function::Count),
@@ -957,6 +1112,62 @@ impl Function {
             "DVARP" => Some(Function::Dvarp),
             "DSTDEVP" => Some(Function::Dstdevp),
 
+            "BETA.DIST" | "_XLFN.BETA.DIST" => Some(Function::BetaDist),
+            "BETA.INV" | "_XLFN.BETA.INV" => Some(Function::BetaInv),
+            "BINOM.DIST" => Some(Function::BinomDist),
+            "BINOM.DIST.RANGE" => Some(Function::BinomDistRange),
+            "BINOM.INV" => Some(Function::BinomInv),
+            "CHISQ.DIST" => Some(Function::ChisqDist),
+            "CHISQ.DIST.RT" => Some(Function::ChisqDistRT),
+            "CHISQ.INV" => Some(Function::ChisqInv),
+            "CHISQ.INV.RT" => Some(Function::ChisqInvRT),
+            "CHISQ.TEST" => Some(Function::ChisqTest),
+            "CONFIDENCE.NORM" => Some(Function::ConfidenceNorm),
+            "CONFIDENCE.T" => Some(Function::ConfidenceT),
+            "COVARIANCE.P" => Some(Function::CovarianceP),
+            "COVARIANCE.S" => Some(Function::CovarianceS),
+            "DEVSQ" => Some(Function::Devsq),
+            "EXPON.DIST" => Some(Function::ExponDist),
+            "F.DIST" => Some(Function::FDist),
+            "F.DIST.RT" => Some(Function::FDistRT),
+            "F.INV" => Some(Function::FInv),
+            "F.INV.RT" => Some(Function::FInvRT),
+            "FISHER" => Some(Function::Fisher),
+            "FISHERINV" => Some(Function::FisherInv),
+            "GAMMA" | "_XLFN.GAMMA" => Some(Function::Gamma),
+            "GAMMA.DIST" | "_XLFN.GAMMA.DIST" => Some(Function::GammaDist),
+            "GAMMA.INV" | "_XLFN.GAMMA.INV" => Some(Function::GammaInv),
+            "GAMMALN" | "_XLFN.GAMMALN" => Some(Function::GammaLn),
+            "GAMMALN.PRECISE" | "_XLFN.GAMMALN.PRECISE" => Some(Function::GammaLnPrecise),
+            "HYPGEOM.DIST" => Some(Function::HypGeomDist),
+            "LOGNORM.DIST" => Some(Function::LogNormDist),
+            "LOGNORM.INV" => Some(Function::LogNormInv),
+            "NEGBINOM.DIST" => Some(Function::NegbinomDist),
+            "NORM.DIST" => Some(Function::NormDist),
+            "NORM.INV" => Some(Function::NormInv),
+            "NORM.S.DIST" => Some(Function::NormSdist),
+            "NORM.S.INV" => Some(Function::NormSInv),
+            "PEARSON" => Some(Function::Pearson),
+            "PHI" => Some(Function::Phi),
+            "POISSON.DIST" => Some(Function::PoissonDist),
+            "STANDARDIZE" => Some(Function::Standardize),
+            "STDEV.P" => Some(Function::StDevP),
+            "STDEV.S" => Some(Function::StDevS),
+            "STDEVA" => Some(Function::Stdeva),
+            "STDEVPA" => Some(Function::Stdevpa),
+            "T.DIST" => Some(Function::TDist),
+            "T.DIST.2T" => Some(Function::TDist2T),
+            "T.DIST.RT" => Some(Function::TDistRT),
+            "T.INV" => Some(Function::TInv),
+            "T.INV.2T" => Some(Function::TInv2T),
+            "T.TEST" => Some(Function::TTest),
+            "VAR.P" => Some(Function::VarP),
+            "VAR.S" => Some(Function::VarS),
+            "VARPA" => Some(Function::VarpA),
+            "VARA" => Some(Function::VarA),
+            "WEIBULL.DIST" => Some(Function::WeibullDist),
+            "Z.TEST" => Some(Function::ZTest),
+
             _ => None,
         }
     }
@@ -1065,6 +1276,7 @@ impl fmt::Display for Function {
             Function::Sheet => write!(f, "SHEET"),
             Function::Average => write!(f, "AVERAGE"),
             Function::Averagea => write!(f, "AVERAGEA"),
+            Function::Avedev => write!(f, "AVEDEV"),
             Function::Averageif => write!(f, "AVERAGEIF"),
             Function::Averageifs => write!(f, "AVERAGEIFS"),
             Function::Count => write!(f, "COUNT"),
@@ -1234,6 +1446,62 @@ impl fmt::Display for Function {
             Function::Dvar => write!(f, "DVAR"),
             Function::Dvarp => write!(f, "DVARP"),
             Function::Dstdevp => write!(f, "DSTDEVP"),
+
+            Function::BetaDist => write!(f, "BETA.DIST"),
+            Function::BetaInv => write!(f, "BETA.INV"),
+            Function::BinomDist => write!(f, "BINOM.DIST"),
+            Function::BinomDistRange => write!(f, "BINOM.DIST.RANGE"),
+            Function::BinomInv => write!(f, "BINOM.INV"),
+            Function::ChisqDist => write!(f, "CHISQ.DIST"),
+            Function::ChisqDistRT => write!(f, "CHISQ.DIST.RT"),
+            Function::ChisqInv => write!(f, "CHISQ.INV"),
+            Function::ChisqInvRT => write!(f, "CHISQ.INV.RT"),
+            Function::ChisqTest => write!(f, "CHISQ.TEST"),
+            Function::ConfidenceNorm => write!(f, "CONFIDENCE.NORM"),
+            Function::ConfidenceT => write!(f, "CONFIDENCE.T"),
+            Function::CovarianceP => write!(f, "COVARIANCE.P"),
+            Function::CovarianceS => write!(f, "COVARIANCE.S"),
+            Function::Devsq => write!(f, "DEVSQ"),
+            Function::ExponDist => write!(f, "EXPON.DIST"),
+            Function::FDist => write!(f, "F.DIST"),
+            Function::FDistRT => write!(f, "F.DIST.RT"),
+            Function::FInv => write!(f, "F.INV"),
+            Function::FInvRT => write!(f, "F.INV.RT"),
+            Function::Fisher => write!(f, "FISHER"),
+            Function::FisherInv => write!(f, "FISHERINV"),
+            Function::Gamma => write!(f, "GAMMA"),
+            Function::GammaDist => write!(f, "GAMMA.DIST"),
+            Function::GammaInv => write!(f, "GAMMA.INV"),
+            Function::GammaLn => write!(f, "GAMMALN"),
+            Function::GammaLnPrecise => write!(f, "GAMMALN.PRECISE"),
+            Function::HypGeomDist => write!(f, "HYPGEOM.DIST"),
+            Function::LogNormDist => write!(f, "LOGNORM.DIST"),
+            Function::LogNormInv => write!(f, "LOGNORM.INV"),
+            Function::NegbinomDist => write!(f, "NEGBINOM.DIST"),
+            Function::NormDist => write!(f, "NORM.DIST"),
+            Function::NormInv => write!(f, "NORM.INV"),
+            Function::NormSdist => write!(f, "NORM.S.DIST"),
+            Function::NormSInv => write!(f, "NORM.S.INV"),
+            Function::Pearson => write!(f, "PEARSON"),
+            Function::Phi => write!(f, "PHI"),
+            Function::PoissonDist => write!(f, "POISSON.DIST"),
+            Function::Standardize => write!(f, "STANDARDIZE"),
+            Function::StDevP => write!(f, "STDEV.P"),
+            Function::StDevS => write!(f, "STDEV.S"),
+            Function::Stdeva => write!(f, "STDEVA"),
+            Function::Stdevpa => write!(f, "STDEVPA"),
+            Function::TDist => write!(f, "T.DIST"),
+            Function::TDist2T => write!(f, "T.DIST.2T"),
+            Function::TDistRT => write!(f, "T.DIST.RT"),
+            Function::TInv => write!(f, "T.INV"),
+            Function::TInv2T => write!(f, "T.INV.2T"),
+            Function::TTest => write!(f, "T.TEST"),
+            Function::VarP => write!(f, "VAR.P"),
+            Function::VarS => write!(f, "VAR.S"),
+            Function::VarpA => write!(f, "VARPA"),
+            Function::VarA => write!(f, "VARA"),
+            Function::WeibullDist => write!(f, "WEIBULL.DIST"),
+            Function::ZTest => write!(f, "Z.TEST"),
         }
     }
 }
@@ -1354,6 +1622,7 @@ impl Model {
             Function::Sheet => self.fn_sheet(args, cell),
             Function::Average => self.fn_average(args, cell),
             Function::Averagea => self.fn_averagea(args, cell),
+            Function::Avedev => self.fn_avedev(args, cell),
             Function::Averageif => self.fn_averageif(args, cell),
             Function::Averageifs => self.fn_averageifs(args, cell),
             Function::Count => self.fn_count(args, cell),
@@ -1530,6 +1799,61 @@ impl Model {
             Function::Dvar => self.fn_dvar(args, cell),
             Function::Dvarp => self.fn_dvarp(args, cell),
             Function::Dstdevp => self.fn_dstdevp(args, cell),
+            Function::BetaDist => self.fn_beta_dist(args, cell),
+            Function::BetaInv => self.fn_beta_inv(args, cell),
+            Function::BinomDist => self.fn_binom_dist(args, cell),
+            Function::BinomDistRange => self.fn_binom_dist_range(args, cell),
+            Function::BinomInv => self.fn_binom_inv(args, cell),
+            Function::ChisqDist => self.fn_chisq_dist(args, cell),
+            Function::ChisqDistRT => self.fn_chisq_dist_rt(args, cell),
+            Function::ChisqInv => self.fn_chisq_inv(args, cell),
+            Function::ChisqInvRT => self.fn_chisq_inv_rt(args, cell),
+            Function::ChisqTest => self.fn_chisq_test(args, cell),
+            Function::ConfidenceNorm => self.fn_confidence_norm(args, cell),
+            Function::ConfidenceT => self.fn_confidence_t(args, cell),
+            Function::CovarianceP => self.fn_covariance_p(args, cell),
+            Function::CovarianceS => self.fn_covariance_s(args, cell),
+            Function::Devsq => self.fn_devsq(args, cell),
+            Function::ExponDist => self.fn_expon_dist(args, cell),
+            Function::FDist => self.fn_f_dist(args, cell),
+            Function::FDistRT => self.fn_f_dist_rt(args, cell),
+            Function::FInv => self.fn_f_inv(args, cell),
+            Function::FInvRT => self.fn_f_inv_rt(args, cell),
+            Function::Fisher => self.fn_fisher(args, cell),
+            Function::FisherInv => self.fn_fisher_inv(args, cell),
+            Function::Gamma => self.fn_gamma(args, cell),
+            Function::GammaDist => self.fn_gamma_dist(args, cell),
+            Function::GammaInv => self.fn_gamma_inv(args, cell),
+            Function::GammaLn => self.fn_gamma_ln(args, cell),
+            Function::GammaLnPrecise => self.fn_gamma_ln_precise(args, cell),
+            Function::HypGeomDist => self.fn_hyp_geom_dist(args, cell),
+            Function::LogNormDist => self.fn_log_norm_dist(args, cell),
+            Function::LogNormInv => self.fn_log_norm_inv(args, cell),
+            Function::NegbinomDist => self.fn_negbinom_dist(args, cell),
+            Function::NormDist => self.fn_norm_dist(args, cell),
+            Function::NormInv => self.fn_norm_inv(args, cell),
+            Function::NormSdist => self.fn_norm_s_dist(args, cell),
+            Function::NormSInv => self.fn_norm_s_inv(args, cell),
+            Function::Pearson => self.fn_pearson(args, cell),
+            Function::Phi => self.fn_phi(args, cell),
+            Function::PoissonDist => self.fn_poisson_dist(args, cell),
+            Function::Standardize => self.fn_standardize(args, cell),
+            Function::StDevP => self.fn_stdev_p(args, cell),
+            Function::StDevS => self.fn_stdev_s(args, cell),
+            Function::Stdeva => self.fn_stdeva(args, cell),
+            Function::Stdevpa => self.fn_stdevpa(args, cell),
+            Function::TDist => self.fn_t_dist(args, cell),
+            Function::TDist2T => self.fn_t_dist_2t(args, cell),
+            Function::TDistRT => self.fn_t_dist_rt(args, cell),
+            Function::TInv => self.fn_t_inv(args, cell),
+            Function::TInv2T => self.fn_t_inv_2t(args, cell),
+            Function::TTest => self.fn_t_test(args, cell),
+            Function::VarP => self.fn_var_p(args, cell),
+            Function::VarS => self.fn_var_s(args, cell),
+            Function::VarpA => self.fn_varpa(args, cell),
+            Function::VarA => self.fn_vara(args, cell),
+            Function::WeibullDist => self.fn_weibull_dist(args, cell),
+            Function::ZTest => self.fn_z_test(args, cell),
         }
     }
 }

base/src/functions/statistical/beta.rs

@@ -0,0 +1,213 @@
+use statrs::distribution::{Beta, Continuous, ContinuousCDF};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // BETA.DIST(x, alpha, beta, cumulative, [A], [B])
+    pub(crate) fn fn_beta_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        let arg_count = args.len();
+        if !(4..=6).contains(&arg_count) {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let alpha = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let beta_param = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // cumulative argument: interpret like Excel
+        let cumulative = match self.evaluate_node_in_context(&args[3], cell) {
+            CalcResult::Boolean(b) => b,
+            CalcResult::Number(n) => n != 0.0,
+            CalcResult::String(s) => {
+                let up = s.to_ascii_uppercase();
+                if up == "TRUE" {
+                    true
+                } else if up == "FALSE" {
+                    false
+                } else {
+                    return CalcResult::Error {
+                        error: Error::VALUE,
+                        origin: cell,
+                        message: "cumulative must be TRUE/FALSE or numeric".to_string(),
+                    };
+                }
+            }
+            error @ CalcResult::Error { .. } => return error,
+            _ => {
+                return CalcResult::Error {
+                    error: Error::VALUE,
+                    origin: cell,
+                    message: "Invalid cumulative argument".to_string(),
+                }
+            }
+        };
+
+        // Optional A, B
+        let a = if arg_count >= 5 {
+            match self.get_number_no_bools(&args[4], cell) {
+                Ok(f) => f,
+                Err(e) => return e,
+            }
+        } else {
+            0.0
+        };
+
+        let b = if arg_count >= 6 {
+            match self.get_number_no_bools(&args[5], cell) {
+                Ok(f) => f,
+                Err(e) => return e,
+            }
+        } else {
+            1.0
+        };
+
+        // Excel: alpha <= 0 or beta <= 0 → #NUM!
+        if alpha <= 0.0 || beta_param <= 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "alpha and beta must be > 0 in BETA.DIST".to_string(),
+            );
+        }
+
+        // Excel: if x < A, x > B, or A = B → #NUM!
+        if b == a || x < a || x > b {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "x must be between A and B and A < B in BETA.DIST".to_string(),
+            );
+        }
+
+        // Transform to standard Beta(0,1)
+        let width = b - a;
+        let t = (x - a) / width;
+
+        let dist = match Beta::new(alpha, beta_param) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Beta distribution".to_string(),
+                )
+            }
+        };
+
+        let result = if cumulative {
+            dist.cdf(t)
+        } else {
+            // general-interval beta pdf: f_X(x) = f_T(t) / (B - A), t=(x-A)/(B-A)
+            dist.pdf(t) / width
+        };
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for BETA.DIST".to_string(),
+            );
+        }
+
+        CalcResult::Number(result)
+    }
+
+    pub(crate) fn fn_beta_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        let arg_count = args.len();
+        if !(3..=5).contains(&arg_count) {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let alpha = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let beta_param = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let a = if arg_count >= 4 {
+            match self.get_number_no_bools(&args[3], cell) {
+                Ok(f) => f,
+                Err(e) => return e,
+            }
+        } else {
+            0.0
+        };
+
+        let b = if arg_count >= 5 {
+            match self.get_number_no_bools(&args[4], cell) {
+                Ok(f) => f,
+                Err(e) => return e,
+            }
+        } else {
+            1.0
+        };
+
+        if alpha <= 0.0 || beta_param <= 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "alpha and beta must be > 0 in BETA.INV".to_string(),
+            );
+        }
+
+        // probability <= 0 or probability > 1 → #NUM!
+        if p <= 0.0 || p > 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "probability must be in (0,1] in BETA.INV".to_string(),
+            );
+        }
+
+        if b <= a {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "A must be < B in BETA.INV".to_string(),
+            );
+        }
+
+        let dist = match Beta::new(alpha, beta_param) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Beta distribution".to_string(),
+                )
+            }
+        };
+
+        let t = dist.inverse_cdf(p);
+        if t.is_nan() || t.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for BETA.INV".to_string(),
+            );
+        }
+
+        // Map back from [0,1] to [A,B]
+        let x = a + t * (b - a);
+        CalcResult::Number(x)
+    }
+}

base/src/functions/statistical/binom.rs

@@ -0,0 +1,311 @@
+use statrs::distribution::{Binomial, Discrete, DiscreteCDF};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_binom_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        // number_s
+        let number_s = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // trials
+        let trials = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // probability_s
+        let p = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // cumulative (logical)
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        // Domain checks
+        if trials < 0.0
+            || number_s < 0.0
+            || number_s > trials
+            || p.is_nan()
+            || !(0.0..=1.0).contains(&p)
+        {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid parameters for BINOM.DIST".to_string(),
+            );
+        }
+
+        // Limit to u64
+        if trials > u64::MAX as f64 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Number of trials too large".to_string(),
+            );
+        }
+
+        let n = trials as u64;
+        let k = number_s as u64;
+
+        let dist = match Binomial::new(p, n) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for binomial distribution".to_string(),
+                )
+            }
+        };
+
+        let prob = if cumulative { dist.cdf(k) } else { dist.pmf(k) };
+
+        if prob.is_nan() || prob.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for BINOM.DIST".to_string(),
+            );
+        }
+
+        CalcResult::Number(prob)
+    }
+
+    pub(crate) fn fn_binom_dist_range(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() < 3 || args.len() > 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        // trials
+        let trials = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // probability_s
+        let p = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // number_s (lower)
+        let number_s = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // number_s2 (upper, optional)
+        let number_s2 = if args.len() == 4 {
+            match self.get_number_no_bools(&args[3], cell) {
+                Ok(f) => f.trunc(),
+                Err(e) => return e,
+            }
+        } else {
+            number_s
+        };
+
+        if trials < 0.0
+            || number_s < 0.0
+            || number_s2 < 0.0
+            || number_s > number_s2
+            || number_s2 > trials
+            || p.is_nan()
+            || !(0.0..=1.0).contains(&p)
+        {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid parameters for BINOM.DIST.RANGE".to_string(),
+            );
+        }
+
+        if trials > u64::MAX as f64 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Number of trials too large".to_string(),
+            );
+        }
+
+        let n = trials as u64;
+        let lower = number_s as u64;
+        let upper = number_s2 as u64;
+
+        let dist = match Binomial::new(p, n) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for binomial distribution".to_string(),
+                )
+            }
+        };
+
+        let prob = if lower == 0 {
+            dist.cdf(upper)
+        } else {
+            let cdf_upper = dist.cdf(upper);
+            let cdf_below_lower = dist.cdf(lower - 1);
+            cdf_upper - cdf_below_lower
+        };
+
+        if prob.is_nan() || prob.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for BINOM.DIST.RANGE".to_string(),
+            );
+        }
+
+        CalcResult::Number(prob)
+    }
+
+    pub(crate) fn fn_binom_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        // trials
+        let trials = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // probability_s
+        let p = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // alpha
+        let alpha = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        if trials < 0.0
+            || trials > u64::MAX as f64
+            || p.is_nan()
+            || !(0.0..=1.0).contains(&p)
+            || alpha.is_nan()
+            || !(0.0..=1.0).contains(&alpha)
+        {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid parameters for BINOM.INV".to_string(),
+            );
+        }
+
+        let n = trials as u64;
+
+        let dist = match Binomial::new(p, n) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for binomial distribution".to_string(),
+                )
+            }
+        };
+
+        // DiscreteCDF::inverse_cdf returns u64 for binomial
+        let k = statrs::distribution::DiscreteCDF::inverse_cdf(&dist, alpha);
+
+        CalcResult::Number(k as f64)
+    }
+
+    pub(crate) fn fn_negbinom_dist(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        use statrs::distribution::{Discrete, DiscreteCDF, NegativeBinomial};
+
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let number_f = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+        let number_s = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+        let probability_s = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if number_f < 0.0 || number_s < 1.0 || !(0.0..=1.0).contains(&probability_s) {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for NEGBINOM.DIST".to_string(),
+            };
+        }
+
+        // Guard against absurdly large failures that won't fit in u64
+        if number_f > (u64::MAX as f64) {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for NEGBINOM.DIST".to_string(),
+            };
+        }
+
+        let dist = match NegativeBinomial::new(number_s, probability_s) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameter for NEGBINOM.DIST".to_string(),
+                }
+            }
+        };
+
+        let f_u = number_f as u64;
+        let result = if cumulative {
+            dist.cdf(f_u)
+        } else {
+            dist.pmf(f_u)
+        };
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for NEGBINOM.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+}

base/src/functions/statistical/chisq.rs

@@ -0,0 +1,565 @@
+use statrs::distribution::{ChiSquared, Continuous, ContinuousCDF};
+
+use crate::expressions::parser::ArrayNode;
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+// Helper to check if two shapes are the same or compatible 1D shapes
+pub(crate) fn is_same_shape_or_1d(rows1: i32, cols1: i32, rows2: i32, cols2: i32) -> bool {
+    (rows1 == rows2 && cols1 == cols2)
+        || (rows1 == 1 && cols2 == 1 && cols1 == rows2)
+        || (rows2 == 1 && cols1 == 1 && cols2 == rows1)
+}
+
+impl Model {
+    // CHISQ.DIST(x, deg_freedom, cumulative)
+    pub(crate) fn fn_chisq_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[2], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "x must be >= 0 in CHISQ.DIST".to_string(),
+            );
+        }
+        if df < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in CHISQ.DIST".to_string(),
+            );
+        }
+
+        let dist = match ChiSquared::new(df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Chi-squared distribution".to_string(),
+                )
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for CHISQ.DIST".to_string(),
+            );
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // CHISQ.DIST.RT(x, deg_freedom)
+    pub(crate) fn fn_chisq_dist_rt(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df_raw = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = df_raw.trunc();
+
+        if x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "x must be >= 0 in CHISQ.DIST.RT".to_string(),
+            );
+        }
+        if df < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in CHISQ.DIST.RT".to_string(),
+            );
+        }
+
+        let dist = match ChiSquared::new(df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Chi-squared distribution".to_string(),
+                )
+            }
+        };
+
+        // Right-tail probability: P(X > x).
+        // Use sf(x) directly for better numerical properties than 1 - cdf(x).
+        let result = dist.sf(x);
+
+        if result.is_nan() || result.is_infinite() || result < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for CHISQ.DIST.RT".to_string(),
+            );
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // CHISQ.INV(probability, deg_freedom)
+    pub(crate) fn fn_chisq_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // if probability < 0 or > 1 → #NUM!
+        if !(0.0..=1.0).contains(&p) {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "probability must be in [0,1] in CHISQ.INV".to_string(),
+            );
+        }
+        if df < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in CHISQ.INV".to_string(),
+            );
+        }
+
+        let dist = match ChiSquared::new(df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Chi-squared distribution".to_string(),
+                )
+            }
+        };
+
+        let x = dist.inverse_cdf(p);
+
+        if x.is_nan() || x.is_infinite() || x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for CHISQ.INV".to_string(),
+            );
+        }
+
+        CalcResult::Number(x)
+    }
+
+    // CHISQ.INV.RT(probability, deg_freedom)
+    pub(crate) fn fn_chisq_inv_rt(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df_raw = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = df_raw.trunc();
+
+        // if probability < 0 or > 1 → #NUM!
+        if !(0.0..=1.0).contains(&p) {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "probability must be in [0,1] in CHISQ.INV.RT".to_string(),
+            );
+        }
+        if df < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in CHISQ.INV.RT".to_string(),
+            );
+        }
+
+        let dist = match ChiSquared::new(df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Chi-squared distribution".to_string(),
+                )
+            }
+        };
+
+        // Right-tail inverse: p = P(X > x) = SF(x) = 1 - CDF(x)
+        // So x = inverse_cdf(1 - p).
+        let x = dist.inverse_cdf(1.0 - p);
+
+        if x.is_nan() || x.is_infinite() || x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for CHISQ.INV.RT".to_string(),
+            );
+        }
+
+        CalcResult::Number(x)
+    }
+
+    pub(crate) fn values_from_range(
+        &mut self,
+        left: CellReferenceIndex,
+        right: CellReferenceIndex,
+    ) -> Result<Vec<Option<f64>>, CalcResult> {
+        let mut values = Vec::new();
+        for row_offset in 0..=(right.row - left.row) {
+            for col_offset in 0..=(right.column - left.column) {
+                let cell_ref = CellReferenceIndex {
+                    sheet: left.sheet,
+                    row: left.row + row_offset,
+                    column: left.column + col_offset,
+                };
+                let cell_value = self.evaluate_cell(cell_ref);
+                match cell_value {
+                    CalcResult::Number(v) => {
+                        values.push(Some(v));
+                    }
+                    error @ CalcResult::Error { .. } => return Err(error),
+                    _ => {
+                        values.push(None);
+                    }
+                }
+            }
+        }
+        Ok(values)
+    }
+
+    pub(crate) fn values_from_array(
+        &mut self,
+        array: Vec<Vec<ArrayNode>>,
+    ) -> Result<Vec<Option<f64>>, Error> {
+        let mut values = Vec::new();
+        for row in array {
+            for item in row {
+                match item {
+                    ArrayNode::Number(f) => {
+                        values.push(Some(f));
+                    }
+                    ArrayNode::Error(error) => {
+                        return Err(error);
+                    }
+                    _ => {
+                        values.push(None);
+                    }
+                }
+            }
+        }
+        Ok(values)
+    }
+
+    // CHISQ.TEST(actual_range, expected_range)
+    pub(crate) fn fn_chisq_test(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+        let actual_range = self.evaluate_node_in_context(&args[0], cell);
+        let expected_range = self.evaluate_node_in_context(&args[1], cell);
+
+        let (width, height, values_left, values_right) = match (actual_range, expected_range) {
+            (
+                CalcResult::Range {
+                    left: l1,
+                    right: r1,
+                },
+                CalcResult::Range {
+                    left: l2,
+                    right: r2,
+                },
+            ) => {
+                if l1.sheet != l2.sheet {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Ranges are in different sheets".to_string(),
+                    );
+                }
+                let rows1 = r1.row - l1.row + 1;
+                let cols1 = r1.column - l1.column + 1;
+                let rows2 = r2.row - l2.row + 1;
+                let cols2 = r2.column - l2.column + 1;
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Ranges must be of the same shape".to_string(),
+                    );
+                }
+                let values_left = match self.values_from_range(l1, r1) {
+                    Err(error) => {
+                        return error;
+                    }
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_range(l2, r2) {
+                    Err(error) => {
+                        return error;
+                    }
+                    Ok(v) => v,
+                };
+                (rows1, cols1, values_left, values_right)
+            }
+            (
+                CalcResult::Array(left),
+                CalcResult::Range {
+                    left: l2,
+                    right: r2,
+                },
+            ) => {
+                let rows2 = r2.row - l2.row + 1;
+                let cols2 = r2.column - l2.column + 1;
+
+                let rows1 = left.len() as i32;
+                let cols1 = if rows1 > 0 { left[0].len() as i32 } else { 0 };
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Array and range must be of the same shape".to_string(),
+                    );
+                }
+                let values_left = match self.values_from_array(left) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in first array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_range(l2, r2) {
+                    Err(error) => {
+                        return error;
+                    }
+                    Ok(v) => v,
+                };
+                (rows2, cols2, values_left, values_right)
+            }
+            (
+                CalcResult::Range {
+                    left: l1,
+                    right: r1,
+                },
+                CalcResult::Array(right),
+            ) => {
+                let rows1 = r1.row - l1.row + 1;
+                let cols1 = r1.column - l1.column + 1;
+
+                let rows2 = right.len() as i32;
+                let cols2 = if rows2 > 0 { right[0].len() as i32 } else { 0 };
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Range and array must be of the same shape".to_string(),
+                    );
+                }
+                let values_left = match self.values_from_range(l1, r1) {
+                    Err(error) => {
+                        return error;
+                    }
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_array(right) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in second array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+                (rows1, cols1, values_left, values_right)
+            }
+            (CalcResult::Array(left), CalcResult::Array(right)) => {
+                let rows1 = left.len() as i32;
+                let rows2 = right.len() as i32;
+                let cols1 = if rows1 > 0 { left[0].len() as i32 } else { 0 };
+                let cols2 = if rows2 > 0 { right[0].len() as i32 } else { 0 };
+
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Arrays must be of the same shape".to_string(),
+                    );
+                }
+                let values_left = match self.values_from_array(left) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in first array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_array(right) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in second array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+                (rows1, cols1, values_left, values_right)
+            }
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Both arguments must be ranges or arrays".to_string(),
+                );
+            }
+        };
+
+        let mut values = Vec::with_capacity(values_left.len());
+
+        // Now we have:
+        // - values: flattened (observed, expected)
+        // - width, height: shape
+        for i in 0..values_left.len() {
+            match (values_left[i], values_right[i]) {
+                (Some(v1), Some(v2)) => {
+                    values.push((v1, v2));
+                }
+                _ => {
+                    values.push((1.0, 1.0));
+                }
+            }
+        }
+        if width == 0 || height == 0 || values.len() < 2 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "CHISQ.TEST requires at least two data points".to_string(),
+            );
+        }
+
+        let mut chi2 = 0.0;
+        for (obs, exp) in &values {
+            if *obs < 0.0 || *exp < 0.0 {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Negative value in CHISQ.TEST data".to_string(),
+                );
+            }
+            if *exp == 0.0 {
+                return CalcResult::new_error(
+                    Error::DIV,
+                    cell,
+                    "Zero expected value in CHISQ.TEST".to_string(),
+                );
+            }
+            let diff = obs - exp;
+            chi2 += (diff * diff) / exp;
+        }
+
+        if chi2 < 0.0 && chi2 > -1e-12 {
+            chi2 = 0.0;
+        }
+
+        let total = width * height;
+        if total <= 1 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "CHISQ.TEST degrees of freedom is zero".to_string(),
+            );
+        }
+
+        let df = if width > 1 && height > 1 {
+            (width - 1) * (height - 1)
+        } else {
+            total - 1
+        };
+
+        let dist = match ChiSquared::new(df as f64) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid degrees of freedom in CHISQ.TEST".to_string(),
+                );
+            }
+        };
+
+        let mut p = 1.0 - dist.cdf(chi2);
+
+        // clamp tiny fp noise
+        if p < 0.0 && p > -1e-15 {
+            p = 0.0;
+        }
+        if p > 1.0 && p < 1.0 + 1e-15 {
+            p = 1.0;
+        }
+
+        CalcResult::Number(p)
+    }
+}

base/src/functions/statistical/count_and_average.rs

@@ -1,14 +1,10 @@
 use crate::constants::{LAST_COLUMN, LAST_ROW};
+use crate::expressions::parser::ArrayNode;
 use crate::expressions::types::CellReferenceIndex;
 use crate::{
-    calc_result::{CalcResult, Range},
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
-    expressions::parser::Node,
-    expressions::token::Error,
-    model::Model,
 };
 
-use super::util::build_criteria;
-
 impl Model {
     pub(crate) fn fn_average(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
         if args.is_empty() {
@@ -90,7 +86,6 @@ impl Model {
         }
         CalcResult::Number(sum / count)
     }
-
     pub(crate) fn fn_averagea(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
         if args.is_empty() {
             return CalcResult::new_args_number_error(cell);
@@ -324,350 +319,26 @@ impl Model {
         CalcResult::Number(result)
     }
 
-    pub(crate) fn fn_countif(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+    pub(crate) fn fn_avedev(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
-        if args.len() == 2 {
-            let arguments = vec![args[0].clone(), args[1].clone()];
-            self.fn_countifs(&arguments, cell)
-        } else {
-            CalcResult::new_args_number_error(cell)
-        }
-    }
-
-    /// AVERAGEIF(criteria_range, criteria, [average_range])
-    /// if average_rage is missing then criteria_range will be used
-    pub(crate) fn fn_averageif(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
-        if args.len() == 2 {
-            let arguments = vec![args[0].clone(), args[0].clone(), args[1].clone()];
-            self.fn_averageifs(&arguments, cell)
-        } else if args.len() == 3 {
-            let arguments = vec![args[2].clone(), args[0].clone(), args[1].clone()];
-            self.fn_averageifs(&arguments, cell)
-        } else {
-            CalcResult::new_args_number_error(cell)
-        }
-    }
-
-    // FIXME: This function shares a lot of code with apply_ifs. Can we merge them?
-    pub(crate) fn fn_countifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
-        let args_count = args.len();
-        if args_count < 2 || !args_count.is_multiple_of(2) {
-            return CalcResult::new_args_number_error(cell);
-        }
-
-        let case_count = args_count / 2;
-        // NB: this is a beautiful example of the borrow checker
-        // The order of these two definitions cannot be swapped.
-        let mut criteria = Vec::new();
-        let mut fn_criteria = Vec::new();
-        let ranges = &mut Vec::new();
-        for case_index in 0..case_count {
-            let criterion = self.evaluate_node_in_context(&args[case_index * 2 + 1], cell);
-            criteria.push(criterion);
-            // NB: We cannot do:
-            // fn_criteria.push(build_criteria(&criterion));
-            // because criterion doesn't live long enough
-            let result = self.evaluate_node_in_context(&args[case_index * 2], cell);
-            if result.is_error() {
-                return result;
-            }
-            if let CalcResult::Range { left, right } = result {
-                if left.sheet != right.sheet {
-                    return CalcResult::new_error(
-                        Error::VALUE,
-                        cell,
-                        "Ranges are in different sheets".to_string(),
-                    );
-                }
-                // TODO test ranges are of the same size as sum_range
-                ranges.push(Range { left, right });
-            } else {
-                return CalcResult::new_error(Error::VALUE, cell, "Expected a range".to_string());
-            }
-        }
-        for criterion in criteria.iter() {
-            fn_criteria.push(build_criteria(criterion));
-        }
-
-        let mut total = 0.0;
-        let first_range = &ranges[0];
-        let left_row = first_range.left.row;
-        let left_column = first_range.left.column;
-        let right_row = first_range.right.row;
-        let right_column = first_range.right.column;
-
-        let dimension = match self.workbook.worksheet(first_range.left.sheet) {
-            Ok(s) => s.dimension(),
-            Err(_) => {
-                return CalcResult::new_error(
-                    Error::ERROR,
-                    cell,
-                    format!("Invalid worksheet index: '{}'", first_range.left.sheet),
-                )
-            }
-        };
-        let max_row = dimension.max_row;
-        let max_column = dimension.max_column;
-
-        let open_row = left_row == 1 && right_row == LAST_ROW;
-        let open_column = left_column == 1 && right_column == LAST_COLUMN;
-
-        for row in left_row..right_row + 1 {
-            if open_row && row > max_row {
-                // If the row is larger than the max row in the sheet then all cells are empty.
-                // We compute it only once
-                let mut is_true = true;
-                for fn_criterion in fn_criteria.iter() {
-                    if !fn_criterion(&CalcResult::EmptyCell) {
-                        is_true = false;
-                        break;
-                    }
-                }
-                if is_true {
-                    total += ((LAST_ROW - max_row) * (right_column - left_column + 1)) as f64;
-                }
-                break;
-            }
-            for column in left_column..right_column + 1 {
-                if open_column && column > max_column {
-                    // If the column is larger than the max column in the sheet then all cells are empty.
-                    // We compute it only once
-                    let mut is_true = true;
-                    for fn_criterion in fn_criteria.iter() {
-                        if !fn_criterion(&CalcResult::EmptyCell) {
-                            is_true = false;
-                            break;
-                        }
-                    }
-                    if is_true {
-                        total += (LAST_COLUMN - max_column) as f64;
-                    }
-                    break;
-                }
-                let mut is_true = true;
-                for case_index in 0..case_count {
-                    // We check if value in range n meets criterion n
-                    let range = &ranges[case_index];
-                    let fn_criterion = &fn_criteria[case_index];
-                    let value = self.evaluate_cell(CellReferenceIndex {
-                        sheet: range.left.sheet,
-                        row: range.left.row + row - first_range.left.row,
-                        column: range.left.column + column - first_range.left.column,
-                    });
-                    if !fn_criterion(&value) {
-                        is_true = false;
-                        break;
-                    }
-                }
-                if is_true {
-                    total += 1.0;
-                }
-            }
-        }
-        CalcResult::Number(total)
-    }
-
-    pub(crate) fn apply_ifs<F>(
-        &mut self,
-        args: &[Node],
-        cell: CellReferenceIndex,
-        mut apply: F,
-    ) -> Result<(), CalcResult>
-    where
-        F: FnMut(f64),
-    {
-        let args_count = args.len();
-        if args_count < 3 || args_count.is_multiple_of(2) {
-            return Err(CalcResult::new_args_number_error(cell));
-        }
-        let arg_0 = self.evaluate_node_in_context(&args[0], cell);
-        if arg_0.is_error() {
-            return Err(arg_0);
-        }
-        let sum_range = if let CalcResult::Range { left, right } = arg_0 {
-            if left.sheet != right.sheet {
-                return Err(CalcResult::new_error(
-                    Error::VALUE,
-                    cell,
-                    "Ranges are in different sheets".to_string(),
-                ));
-            }
-            Range { left, right }
-        } else {
-            return Err(CalcResult::new_error(
-                Error::VALUE,
-                cell,
-                "Expected a range".to_string(),
-            ));
-        };
-
-        let case_count = (args_count - 1) / 2;
-        // NB: this is a beautiful example of the borrow checker
-        // The order of these two definitions cannot be swapped.
-        let mut criteria = Vec::new();
-        let mut fn_criteria = Vec::new();
-        let ranges = &mut Vec::new();
-        for case_index in 1..=case_count {
-            let criterion = self.evaluate_node_in_context(&args[case_index * 2], cell);
-            // NB: criterion might be an error. That's ok
-            criteria.push(criterion);
-            // NB: We cannot do:
-            // fn_criteria.push(build_criteria(&criterion));
-            // because criterion doesn't live long enough
-            let result = self.evaluate_node_in_context(&args[case_index * 2 - 1], cell);
-            if result.is_error() {
-                return Err(result);
-            }
-            if let CalcResult::Range { left, right } = result {
-                if left.sheet != right.sheet {
-                    return Err(CalcResult::new_error(
-                        Error::VALUE,
-                        cell,
-                        "Ranges are in different sheets".to_string(),
-                    ));
-                }
-                // TODO test ranges are of the same size as sum_range
-                ranges.push(Range { left, right });
-            } else {
-                return Err(CalcResult::new_error(
-                    Error::VALUE,
-                    cell,
-                    "Expected a range".to_string(),
-                ));
-            }
-        }
-        for criterion in criteria.iter() {
-            fn_criteria.push(build_criteria(criterion));
-        }
-
-        let left_row = sum_range.left.row;
-        let left_column = sum_range.left.column;
-        let mut right_row = sum_range.right.row;
-        let mut right_column = sum_range.right.column;
-
-        if left_row == 1 && right_row == LAST_ROW {
-            right_row = match self.workbook.worksheet(sum_range.left.sheet) {
-                Ok(s) => s.dimension().max_row,
-                Err(_) => {
-                    return Err(CalcResult::new_error(
-                        Error::ERROR,
-                        cell,
-                        format!("Invalid worksheet index: '{}'", sum_range.left.sheet),
-                    ));
-                }
-            };
-        }
-        if left_column == 1 && right_column == LAST_COLUMN {
-            right_column = match self.workbook.worksheet(sum_range.left.sheet) {
-                Ok(s) => s.dimension().max_column,
-                Err(_) => {
-                    return Err(CalcResult::new_error(
-                        Error::ERROR,
-                        cell,
-                        format!("Invalid worksheet index: '{}'", sum_range.left.sheet),
-                    ));
-                }
-            };
-        }
-
-        for row in left_row..right_row + 1 {
-            for column in left_column..right_column + 1 {
-                let mut is_true = true;
-                for case_index in 0..case_count {
-                    // We check if value in range n meets criterion n
-                    let range = &ranges[case_index];
-                    let fn_criterion = &fn_criteria[case_index];
-                    let value = self.evaluate_cell(CellReferenceIndex {
-                        sheet: range.left.sheet,
-                        row: range.left.row + row - sum_range.left.row,
-                        column: range.left.column + column - sum_range.left.column,
-                    });
-                    if !fn_criterion(&value) {
-                        is_true = false;
-                        break;
-                    }
-                }
-                if is_true {
-                    let v = self.evaluate_cell(CellReferenceIndex {
-                        sheet: sum_range.left.sheet,
-                        row,
-                        column,
-                    });
-                    match v {
-                        CalcResult::Number(n) => apply(n),
-                        CalcResult::Error { .. } => return Err(v),
-                        _ => {}
-                    }
-                }
-            }
-        }
-        Ok(())
-    }
-
-    pub(crate) fn fn_averageifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
-        let mut total = 0.0;
-        let mut count = 0.0;
-
-        let average = |value: f64| {
-            total += value;
-            count += 1.0;
-        };
-        if let Err(e) = self.apply_ifs(args, cell, average) {
-            return e;
-        }
-
-        if count == 0.0 {
-            return CalcResult::Error {
-                error: Error::DIV,
-                origin: cell,
-                message: "division by 0".to_string(),
-            };
-        }
-        CalcResult::Number(total / count)
-    }
-
-    pub(crate) fn fn_minifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
-        let mut min = f64::INFINITY;
-        let apply_min = |value: f64| min = value.min(min);
-        if let Err(e) = self.apply_ifs(args, cell, apply_min) {
-            return e;
-        }
-
-        if min.is_infinite() {
-            min = 0.0;
-        }
-        CalcResult::Number(min)
-    }
-
-    pub(crate) fn fn_maxifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
-        let mut max = -f64::INFINITY;
-        let apply_max = |value: f64| max = value.max(max);
-        if let Err(e) = self.apply_ifs(args, cell, apply_max) {
-            return e;
-        }
-        if max.is_infinite() {
-            max = 0.0;
-        }
-        CalcResult::Number(max)
-    }
-
-    pub(crate) fn fn_geomean(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
         if args.is_empty() {
             return CalcResult::new_args_number_error(cell);
         }
-        let mut count = 0.0;
+
-        let mut product = 1.0;
+        let mut values: Vec<f64> = Vec::new();
+        let mut sum = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(values: &mut Vec<f64>, sum: &mut f64, count: &mut u64, value: f64) {
+            values.push(value);
+            *sum += value;
+            *count += 1;
+        }
+
         for arg in args {
             match self.evaluate_node_in_context(arg, cell) {
                 CalcResult::Number(value) => {
-                    count += 1.0;
+                    accumulate(&mut values, &mut sum, &mut count, value);
-                    product *= value;
-                }
-                CalcResult::Boolean(b) => {
-                    if let Node::ReferenceKind { .. } = arg {
-                    } else {
-                        product *= if b { 1.0 } else { 0.0 };
-                        count += 1.0;
-                    }
                 }
                 CalcResult::Range { left, right } => {
                     if left.sheet != right.sheet {
@@ -677,57 +348,99 @@ impl Model {
                             "Ranges are in different sheets".to_string(),
                         );
                     }
-                    for row in left.row..(right.row + 1) {
+
-                        for column in left.column..(right.column + 1) {
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..=row2 {
+                        for column in column1..=column2 {
                             match self.evaluate_cell(CellReferenceIndex {
                                 sheet: left.sheet,
                                 row,
                                 column,
                             }) {
                                 CalcResult::Number(value) => {
-                                    count += 1.0;
+                                    accumulate(&mut values, &mut sum, &mut count, value);
-                                    product *= value;
                                 }
                                 error @ CalcResult::Error { .. } => return error,
-                                CalcResult::Range { .. } => {
+                                _ => {
-                                    return CalcResult::new_error(
+                                    // ignore non-numeric
-                                        Error::ERROR,
+                                }
-                                        cell,
+                            }
-                                        "Unexpected Range".to_string(),
+                        }
-                                    );
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut values, &mut sum, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric
                                 }
-                                _ => {}
                             }
                         }
                     }
                 }
                 error @ CalcResult::Error { .. } => return error,
-                CalcResult::String(s) => {
-                    if let Node::ReferenceKind { .. } = arg {
-                        // Do nothing
-                    } else if let Ok(t) = s.parse::<f64>() {
-                        product *= t;
-                        count += 1.0;
-                    } else {
-                        return CalcResult::Error {
-                            error: Error::VALUE,
-                            origin: cell,
-                            message: "Argument cannot be cast into number".to_string(),
-                        };
-                    }
-                }
                 _ => {
-                    // Ignore everything else
+                    // ignore non-numeric
                 }
-            };
+            }
         }
-        if count == 0.0 {
+
-            return CalcResult::Error {
+        if count == 0 {
-                error: Error::DIV,
+            return CalcResult::new_error(
-                origin: cell,
+                Error::DIV,
-                message: "Division by Zero".to_string(),
+                cell,
-            };
+                "AVEDEV with no numeric data".to_string(),
+            );
         }
-        CalcResult::Number(product.powf(1.0 / count))
+
+        let n = count as f64;
+        let mean = sum / n;
+
+        let mut sum_abs_dev = 0.0;
+        for v in &values {
+            sum_abs_dev += (v - mean).abs();
+        }
+
+        CalcResult::Number(sum_abs_dev / n)
     }
 }

base/src/functions/statistical/covariance.rs

@@ -0,0 +1,264 @@
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_covariance_p(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let values1_opts = match self.evaluate_node_in_context(&args[0], cell) {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(a) => match self.values_from_array(a) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in first array: {:?}", error),
+                    );
+                }
+            },
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "First argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        let values2_opts = match self.evaluate_node_in_context(&args[1], cell) {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(a) => match self.values_from_array(a) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in second array: {:?}", error),
+                    );
+                }
+            },
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Second argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        // Same number of cells
+        if values1_opts.len() != values2_opts.len() {
+            return CalcResult::new_error(
+                Error::NA,
+                cell,
+                "COVARIANCE.P requires arrays of the same size".to_string(),
+            );
+        }
+
+        // Count numeric data points in each array (ignoring text/booleans/empty)
+        let count1 = values1_opts.iter().filter(|v| v.is_some()).count();
+        let count2 = values2_opts.iter().filter(|v| v.is_some()).count();
+
+        if count1 == 0 || count2 == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "COVARIANCE.P requires at least one numeric value in each array".to_string(),
+            );
+        }
+
+        if count1 != count2 {
+            return CalcResult::new_error(
+                Error::NA,
+                cell,
+                "COVARIANCE.P arrays must have the same number of numeric data points".to_string(),
+            );
+        }
+
+        // Build paired numeric vectors, position by position
+        let mut xs: Vec<f64> = Vec::with_capacity(count1);
+        let mut ys: Vec<f64> = Vec::with_capacity(count2);
+
+        for (v1_opt, v2_opt) in values1_opts.into_iter().zip(values2_opts.into_iter()) {
+            if let (Some(x), Some(y)) = (v1_opt, v2_opt) {
+                xs.push(x);
+                ys.push(y);
+            }
+        }
+
+        let n = xs.len();
+        if n == 0 {
+            // Should be impossible given the checks above, but guard anyway
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "COVARIANCE.P has no paired numeric data points".to_string(),
+            );
+        }
+
+        let n_f = n as f64;
+
+        let mut sum_x = 0.0;
+        let mut sum_y = 0.0;
+        for i in 0..n {
+            sum_x += xs[i];
+            sum_y += ys[i];
+        }
+        let mean_x = sum_x / n_f;
+        let mean_y = sum_y / n_f;
+
+        let mut sum_prod = 0.0;
+        for i in 0..n {
+            let dx = xs[i] - mean_x;
+            let dy = ys[i] - mean_y;
+            sum_prod += dx * dy;
+        }
+
+        let cov = sum_prod / n_f;
+        CalcResult::Number(cov)
+    }
+
+    pub(crate) fn fn_covariance_s(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let values1_opts = match self.evaluate_node_in_context(&args[0], cell) {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(a) => match self.values_from_array(a) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in first array: {:?}", error),
+                    );
+                }
+            },
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "First argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        let values2_opts = match self.evaluate_node_in_context(&args[1], cell) {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(a) => match self.values_from_array(a) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in second array: {:?}", error),
+                    );
+                }
+            },
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Second argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        // Same number of cells
+        if values1_opts.len() != values2_opts.len() {
+            return CalcResult::new_error(
+                Error::NA,
+                cell,
+                "COVARIANCE.S requires arrays of the same size".to_string(),
+            );
+        }
+
+        // Count numeric data points in each array (ignoring text/booleans/empty)
+        let count1 = values1_opts.iter().filter(|v| v.is_some()).count();
+        let count2 = values2_opts.iter().filter(|v| v.is_some()).count();
+
+        if count1 == 0 || count2 == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "COVARIANCE.S requires numeric values in each array".to_string(),
+            );
+        }
+
+        if count1 != count2 {
+            return CalcResult::new_error(
+                Error::NA,
+                cell,
+                "COVARIANCE.S arrays must have the same number of numeric data points".to_string(),
+            );
+        }
+
+        // Build paired numeric vectors
+        let mut xs: Vec<f64> = Vec::with_capacity(count1);
+        let mut ys: Vec<f64> = Vec::with_capacity(count2);
+
+        for (v1_opt, v2_opt) in values1_opts.into_iter().zip(values2_opts.into_iter()) {
+            if let (Some(x), Some(y)) = (v1_opt, v2_opt) {
+                xs.push(x);
+                ys.push(y);
+            }
+        }
+
+        let n = xs.len();
+        if n < 2 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "COVARIANCE.S requires at least two paired data points".to_string(),
+            );
+        }
+
+        let n_f = n as f64;
+
+        let mut sum_x = 0.0;
+        let mut sum_y = 0.0;
+        for i in 0..n {
+            sum_x += xs[i];
+            sum_y += ys[i];
+        }
+        let mean_x = sum_x / n_f;
+        let mean_y = sum_y / n_f;
+
+        let mut sum_prod = 0.0;
+        for i in 0..n {
+            let dx = xs[i] - mean_x;
+            let dy = ys[i] - mean_y;
+            sum_prod += dx * dy;
+        }
+
+        let cov = sum_prod / (n_f - 1.0);
+
+        CalcResult::Number(cov)
+    }
+}

base/src/functions/statistical/devsq.rs

@@ -0,0 +1,135 @@
+use crate::constants::{LAST_COLUMN, LAST_ROW};
+use crate::expressions::parser::ArrayNode;
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // DEVSQ(number1, [number2], ...)
+    pub(crate) fn fn_devsq(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        // tiny helper so we don't repeat ourselves
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // We ignore booleans and strings
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // We ignore booleans and strings
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // We ignore booleans and strings
+                }
+            };
+        }
+
+        if count == 0 {
+            // No numeric data at all
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "DEVSQ with no numeric data".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut result = sumsq - (sum * sum) / n;
+
+        // Numerical noise can make result slightly negative when it should be 0
+        if result < 0.0 && result > -1e-12 {
+            result = 0.0;
+        }
+
+        CalcResult::Number(result)
+    }
+}

base/src/functions/statistical/exponential.rs

@@ -0,0 +1,54 @@
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_expon_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        // EXPON.DIST(x, lambda, cumulative)
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let lambda = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[2], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if x < 0.0 || lambda <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for EXPON.DIST".to_string(),
+            };
+        }
+
+        let result = if cumulative {
+            // CDF
+            1.0 - (-lambda * x).exp()
+        } else {
+            // PDF
+            lambda * (-lambda * x).exp()
+        };
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for EXPON.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+}

base/src/functions/statistical/fisher.rs

@@ -0,0 +1,299 @@
+use statrs::distribution::{Continuous, ContinuousCDF, FisherSnedecor};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // FISHER(x) = 0.5 * ln((1 + x) / (1 - x))
+    pub(crate) fn fn_fisher(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 1 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        if x <= -1.0 || x >= 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "x must be between -1 and 1 (exclusive) in FISHER".to_string(),
+            };
+        }
+
+        let ratio = (1.0 + x) / (1.0 - x);
+        let result = 0.5 * ratio.ln();
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for FISHER".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // FISHERINV(y) = (e^(2y) - 1) / (e^(2y) + 1) = tanh(y)
+    pub(crate) fn fn_fisher_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 1 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let y = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // Use tanh directly to avoid overflow from exp(2y)
+        let result = y.tanh();
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for FISHERINV".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // F.DIST(x, deg_freedom1, deg_freedom2, cumulative)
+    pub(crate) fn fn_f_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df1 = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+        let df2 = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        // Excel domain checks
+        if x < 0.0 {
+            return CalcResult::new_error(Error::NUM, cell, "x must be >= 0 in F.DIST".to_string());
+        }
+        if df1 < 1.0 || df2 < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in F.DIST".to_string(),
+            );
+        }
+
+        let dist = match FisherSnedecor::new(df1, df2) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for F distribution".to_string(),
+                )
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for F.DIST".to_string(),
+            );
+        }
+
+        CalcResult::Number(result)
+    }
+
+    pub(crate) fn fn_f_dist_rt(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        // F.DIST.RT(x, deg_freedom1, deg_freedom2)
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df1 = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+        let df2 = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        if x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "x must be >= 0 in F.DIST.RT".to_string(),
+            );
+        }
+        if df1 < 1.0 || df2 < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in F.DIST.RT".to_string(),
+            );
+        }
+
+        let dist = match FisherSnedecor::new(df1, df2) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for F distribution".to_string(),
+                )
+            }
+        };
+
+        // Right-tail probability: P(F > x) = 1 - CDF(x)
+        let result = 1.0 - dist.cdf(x);
+
+        if result.is_nan() || result.is_infinite() || result < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for F.DIST.RT".to_string(),
+            );
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // F.INV(probability, deg_freedom1, deg_freedom2)
+    pub(crate) fn fn_f_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let df1 = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+        let df2 = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // probability < 0 or > 1 → #NUM!
+        if !(0.0..=1.0).contains(&p) {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "probability must be in [0,1] in F.INV".to_string(),
+            );
+        }
+        if df1 < 1.0 || df2 < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in F.INV".to_string(),
+            );
+        }
+
+        let dist = match FisherSnedecor::new(df1, df2) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for F distribution".to_string(),
+                )
+            }
+        };
+
+        let x = dist.inverse_cdf(p);
+        if x.is_nan() || x.is_infinite() || x < 0.0 {
+            return CalcResult::new_error(Error::NUM, cell, "Invalid result for F.INV".to_string());
+        }
+
+        CalcResult::Number(x)
+    }
+
+    // F.INV.RT(probability, deg_freedom1, deg_freedom2)
+    pub(crate) fn fn_f_inv_rt(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let df1 = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+        let df2 = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        if p <= 0.0 || p > 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "probability must be in (0,1] in F.INV.RT".to_string(),
+            );
+        }
+        if df1 < 1.0 || df2 < 1.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "degrees of freedom must be >= 1 in F.INV.RT".to_string(),
+            );
+        }
+
+        let dist = match FisherSnedecor::new(df1, df2) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for F distribution".to_string(),
+                )
+            }
+        };
+
+        // p is right-tail: p = P(F > x) = 1 - CDF(x)
+        let x = dist.inverse_cdf(1.0 - p);
+        if x.is_nan() || x.is_infinite() || x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for F.INV.RT".to_string(),
+            );
+        }
+
+        CalcResult::Number(x)
+    }
+}

base/src/functions/statistical/gamma.rs

@@ -0,0 +1,194 @@
+use statrs::distribution::{Continuous, ContinuousCDF, Gamma};
+use statrs::function::gamma::{gamma, ln_gamma};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_gamma(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 1 {
+            return CalcResult::new_args_number_error(cell);
+        }
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(s) => return s,
+        };
+        if x < 0.0 && x.floor() == x {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for Gamma function".to_string(),
+            };
+        }
+        let result = gamma(x);
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for Gamma function".to_string(),
+            };
+        }
+        CalcResult::Number(result)
+    }
+
+    pub(crate) fn fn_gamma_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        // GAMMA.DIST(x, alpha, beta, cumulative)
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let alpha = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let beta_scale = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "x must be >= 0 in GAMMA.DIST".to_string(),
+            );
+        }
+        if alpha <= 0.0 || beta_scale <= 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "alpha and beta must be > 0 in GAMMA.DIST".to_string(),
+            );
+        }
+
+        let rate = 1.0 / beta_scale;
+
+        let dist = match Gamma::new(alpha, rate) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Gamma distribution".to_string(),
+                )
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for GAMMA.DIST".to_string(),
+            );
+        }
+
+        CalcResult::Number(result)
+    }
+
+    pub(crate) fn fn_gamma_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        // GAMMA.INV(probability, alpha, beta)
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let alpha = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let beta_scale = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        if !(0.0..=1.0).contains(&p) {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "probability must be in [0,1] in GAMMA.INV".to_string(),
+            );
+        }
+
+        if alpha <= 0.0 || beta_scale <= 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "alpha and beta must be > 0 in GAMMA.INV".to_string(),
+            );
+        }
+
+        let rate = 1.0 / beta_scale;
+
+        let dist = match Gamma::new(alpha, rate) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Gamma distribution".to_string(),
+                )
+            }
+        };
+
+        let x = dist.inverse_cdf(p);
+        if x.is_nan() || x.is_infinite() || x < 0.0 {
+            return CalcResult::new_error(
+                Error::NUM,
+                cell,
+                "Invalid result for GAMMA.INV".to_string(),
+            );
+        }
+
+        CalcResult::Number(x)
+    }
+
+    pub(crate) fn fn_gamma_ln(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 1 {
+            return CalcResult::new_args_number_error(cell);
+        }
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(s) => return s,
+        };
+        if x < 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for Gamma function".to_string(),
+            };
+        }
+        let result = ln_gamma(x);
+        if result.is_nan() || result.is_infinite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for Gamma Ln function".to_string(),
+            };
+        }
+        CalcResult::Number(result)
+    }
+
+    pub(crate) fn fn_gamma_ln_precise(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        self.fn_gamma_ln(args, cell)
+    }
+}

base/src/functions/statistical/geomean.rs

@@ -0,0 +1,87 @@
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_geomean(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+        let mut count = 0.0;
+        let mut product = 1.0;
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    count += 1.0;
+                    product *= value;
+                }
+                CalcResult::Boolean(b) => {
+                    if let Node::ReferenceKind { .. } = arg {
+                    } else {
+                        product *= if b { 1.0 } else { 0.0 };
+                        count += 1.0;
+                    }
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+                    for row in left.row..(right.row + 1) {
+                        for column in left.column..(right.column + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    count += 1.0;
+                                    product *= value;
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                CalcResult::Range { .. } => {
+                                    return CalcResult::new_error(
+                                        Error::ERROR,
+                                        cell,
+                                        "Unexpected Range".to_string(),
+                                    );
+                                }
+                                _ => {}
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                CalcResult::String(s) => {
+                    if let Node::ReferenceKind { .. } = arg {
+                        // Do nothing
+                    } else if let Ok(t) = s.parse::<f64>() {
+                        product *= t;
+                        count += 1.0;
+                    } else {
+                        return CalcResult::Error {
+                            error: Error::VALUE,
+                            origin: cell,
+                            message: "Argument cannot be cast into number".to_string(),
+                        };
+                    }
+                }
+                _ => {
+                    // Ignore everything else
+                }
+            };
+        }
+        if count == 0.0 {
+            return CalcResult::Error {
+                error: Error::DIV,
+                origin: cell,
+                message: "Division by Zero".to_string(),
+            };
+        }
+        CalcResult::Number(product.powf(1.0 / count))
+    }
+}

base/src/functions/statistical/hypegeom.rs

@@ -0,0 +1,108 @@
+use statrs::distribution::{Discrete, DiscreteCDF, Hypergeometric};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // =HYPGEOM.DIST(sample_s, number_sample, population_s, number_pop, cumulative)
+    pub(crate) fn fn_hyp_geom_dist(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 5 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        // sample_s (number of successes in the sample)
+        let sample_s = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // number_sample (sample size)
+        let number_sample = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // population_s (number of successes in the population)
+        let population_s = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // number_pop (population size)
+        let number_pop = match self.get_number_no_bools(&args[3], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[4], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if sample_s < 0.0 || sample_s > f64::min(number_sample, population_s) {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for HYPGEOM.DIST".to_string(),
+            };
+        }
+
+        if sample_s < f64::max(0.0, number_sample + population_s - number_pop) {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for HYPGEOM.DIST".to_string(),
+            };
+        }
+
+        if number_sample <= 0.0 || number_sample > number_pop {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for HYPGEOM.DIST".to_string(),
+            };
+        }
+
+        if population_s <= 0.0 || population_s > number_pop {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for HYPGEOM.DIST".to_string(),
+            };
+        }
+
+        let n_pop = number_pop as u64;
+        let k_pop = population_s as u64;
+        let n_sample = number_sample as u64;
+        let k = sample_s as u64;
+
+        let dist = match Hypergeometric::new(n_pop, k_pop, n_sample) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for hypergeometric distribution".to_string(),
+                )
+            }
+        };
+
+        let prob = if cumulative { dist.cdf(k) } else { dist.pmf(k) };
+
+        if !prob.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for HYPGEOM.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(prob)
+    }
+}

base/src/functions/statistical/if_ifs.rs

@@ -0,0 +1,337 @@
+use crate::constants::{LAST_COLUMN, LAST_ROW};
+use crate::expressions::types::CellReferenceIndex;
+use crate::functions::util::build_criteria;
+use crate::{
+    calc_result::{CalcResult, Range},
+    expressions::parser::Node,
+    expressions::token::Error,
+    model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_countif(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() == 2 {
+            let arguments = vec![args[0].clone(), args[1].clone()];
+            self.fn_countifs(&arguments, cell)
+        } else {
+            CalcResult::new_args_number_error(cell)
+        }
+    }
+
+    /// AVERAGEIF(criteria_range, criteria, [average_range])
+    /// if average_rage is missing then criteria_range will be used
+    pub(crate) fn fn_averageif(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() == 2 {
+            let arguments = vec![args[0].clone(), args[0].clone(), args[1].clone()];
+            self.fn_averageifs(&arguments, cell)
+        } else if args.len() == 3 {
+            let arguments = vec![args[2].clone(), args[0].clone(), args[1].clone()];
+            self.fn_averageifs(&arguments, cell)
+        } else {
+            CalcResult::new_args_number_error(cell)
+        }
+    }
+
+    // FIXME: This function shares a lot of code with apply_ifs. Can we merge them?
+    pub(crate) fn fn_countifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        let args_count = args.len();
+        if args_count < 2 || !args_count.is_multiple_of(2) {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let case_count = args_count / 2;
+        // NB: this is a beautiful example of the borrow checker
+        // The order of these two definitions cannot be swapped.
+        let mut criteria = Vec::new();
+        let mut fn_criteria = Vec::new();
+        let ranges = &mut Vec::new();
+        for case_index in 0..case_count {
+            let criterion = self.evaluate_node_in_context(&args[case_index * 2 + 1], cell);
+            criteria.push(criterion);
+            // NB: We cannot do:
+            // fn_criteria.push(build_criteria(&criterion));
+            // because criterion doesn't live long enough
+            let result = self.evaluate_node_in_context(&args[case_index * 2], cell);
+            if result.is_error() {
+                return result;
+            }
+            if let CalcResult::Range { left, right } = result {
+                if left.sheet != right.sheet {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Ranges are in different sheets".to_string(),
+                    );
+                }
+                // TODO test ranges are of the same size as sum_range
+                ranges.push(Range { left, right });
+            } else {
+                return CalcResult::new_error(Error::VALUE, cell, "Expected a range".to_string());
+            }
+        }
+        for criterion in criteria.iter() {
+            fn_criteria.push(build_criteria(criterion));
+        }
+
+        let mut total = 0.0;
+        let first_range = &ranges[0];
+        let left_row = first_range.left.row;
+        let left_column = first_range.left.column;
+        let right_row = first_range.right.row;
+        let right_column = first_range.right.column;
+
+        let dimension = match self.workbook.worksheet(first_range.left.sheet) {
+            Ok(s) => s.dimension(),
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::ERROR,
+                    cell,
+                    format!("Invalid worksheet index: '{}'", first_range.left.sheet),
+                )
+            }
+        };
+        let max_row = dimension.max_row;
+        let max_column = dimension.max_column;
+
+        let open_row = left_row == 1 && right_row == LAST_ROW;
+        let open_column = left_column == 1 && right_column == LAST_COLUMN;
+
+        for row in left_row..right_row + 1 {
+            if open_row && row > max_row {
+                // If the row is larger than the max row in the sheet then all cells are empty.
+                // We compute it only once
+                let mut is_true = true;
+                for fn_criterion in fn_criteria.iter() {
+                    if !fn_criterion(&CalcResult::EmptyCell) {
+                        is_true = false;
+                        break;
+                    }
+                }
+                if is_true {
+                    total += ((LAST_ROW - max_row) * (right_column - left_column + 1)) as f64;
+                }
+                break;
+            }
+            for column in left_column..right_column + 1 {
+                if open_column && column > max_column {
+                    // If the column is larger than the max column in the sheet then all cells are empty.
+                    // We compute it only once
+                    let mut is_true = true;
+                    for fn_criterion in fn_criteria.iter() {
+                        if !fn_criterion(&CalcResult::EmptyCell) {
+                            is_true = false;
+                            break;
+                        }
+                    }
+                    if is_true {
+                        total += (LAST_COLUMN - max_column) as f64;
+                    }
+                    break;
+                }
+                let mut is_true = true;
+                for case_index in 0..case_count {
+                    // We check if value in range n meets criterion n
+                    let range = &ranges[case_index];
+                    let fn_criterion = &fn_criteria[case_index];
+                    let value = self.evaluate_cell(CellReferenceIndex {
+                        sheet: range.left.sheet,
+                        row: range.left.row + row - first_range.left.row,
+                        column: range.left.column + column - first_range.left.column,
+                    });
+                    if !fn_criterion(&value) {
+                        is_true = false;
+                        break;
+                    }
+                }
+                if is_true {
+                    total += 1.0;
+                }
+            }
+        }
+        CalcResult::Number(total)
+    }
+
+    pub(crate) fn apply_ifs<F>(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+        mut apply: F,
+    ) -> Result<(), CalcResult>
+    where
+        F: FnMut(f64),
+    {
+        let args_count = args.len();
+        if args_count < 3 || args_count.is_multiple_of(2) {
+            return Err(CalcResult::new_args_number_error(cell));
+        }
+        let arg_0 = self.evaluate_node_in_context(&args[0], cell);
+        if arg_0.is_error() {
+            return Err(arg_0);
+        }
+        let sum_range = if let CalcResult::Range { left, right } = arg_0 {
+            if left.sheet != right.sheet {
+                return Err(CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Ranges are in different sheets".to_string(),
+                ));
+            }
+            Range { left, right }
+        } else {
+            return Err(CalcResult::new_error(
+                Error::VALUE,
+                cell,
+                "Expected a range".to_string(),
+            ));
+        };
+
+        let case_count = (args_count - 1) / 2;
+        // NB: this is a beautiful example of the borrow checker
+        // The order of these two definitions cannot be swapped.
+        let mut criteria = Vec::new();
+        let mut fn_criteria = Vec::new();
+        let ranges = &mut Vec::new();
+        for case_index in 1..=case_count {
+            let criterion = self.evaluate_node_in_context(&args[case_index * 2], cell);
+            // NB: criterion might be an error. That's ok
+            criteria.push(criterion);
+            // NB: We cannot do:
+            // fn_criteria.push(build_criteria(&criterion));
+            // because criterion doesn't live long enough
+            let result = self.evaluate_node_in_context(&args[case_index * 2 - 1], cell);
+            if result.is_error() {
+                return Err(result);
+            }
+            if let CalcResult::Range { left, right } = result {
+                if left.sheet != right.sheet {
+                    return Err(CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Ranges are in different sheets".to_string(),
+                    ));
+                }
+                // TODO test ranges are of the same size as sum_range
+                ranges.push(Range { left, right });
+            } else {
+                return Err(CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Expected a range".to_string(),
+                ));
+            }
+        }
+        for criterion in criteria.iter() {
+            fn_criteria.push(build_criteria(criterion));
+        }
+
+        let left_row = sum_range.left.row;
+        let left_column = sum_range.left.column;
+        let mut right_row = sum_range.right.row;
+        let mut right_column = sum_range.right.column;
+
+        if left_row == 1 && right_row == LAST_ROW {
+            right_row = match self.workbook.worksheet(sum_range.left.sheet) {
+                Ok(s) => s.dimension().max_row,
+                Err(_) => {
+                    return Err(CalcResult::new_error(
+                        Error::ERROR,
+                        cell,
+                        format!("Invalid worksheet index: '{}'", sum_range.left.sheet),
+                    ));
+                }
+            };
+        }
+        if left_column == 1 && right_column == LAST_COLUMN {
+            right_column = match self.workbook.worksheet(sum_range.left.sheet) {
+                Ok(s) => s.dimension().max_column,
+                Err(_) => {
+                    return Err(CalcResult::new_error(
+                        Error::ERROR,
+                        cell,
+                        format!("Invalid worksheet index: '{}'", sum_range.left.sheet),
+                    ));
+                }
+            };
+        }
+
+        for row in left_row..right_row + 1 {
+            for column in left_column..right_column + 1 {
+                let mut is_true = true;
+                for case_index in 0..case_count {
+                    // We check if value in range n meets criterion n
+                    let range = &ranges[case_index];
+                    let fn_criterion = &fn_criteria[case_index];
+                    let value = self.evaluate_cell(CellReferenceIndex {
+                        sheet: range.left.sheet,
+                        row: range.left.row + row - sum_range.left.row,
+                        column: range.left.column + column - sum_range.left.column,
+                    });
+                    if !fn_criterion(&value) {
+                        is_true = false;
+                        break;
+                    }
+                }
+                if is_true {
+                    let v = self.evaluate_cell(CellReferenceIndex {
+                        sheet: sum_range.left.sheet,
+                        row,
+                        column,
+                    });
+                    match v {
+                        CalcResult::Number(n) => apply(n),
+                        CalcResult::Error { .. } => return Err(v),
+                        _ => {}
+                    }
+                }
+            }
+        }
+        Ok(())
+    }
+
+    pub(crate) fn fn_averageifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        let mut total = 0.0;
+        let mut count = 0.0;
+
+        let average = |value: f64| {
+            total += value;
+            count += 1.0;
+        };
+        if let Err(e) = self.apply_ifs(args, cell, average) {
+            return e;
+        }
+
+        if count == 0.0 {
+            return CalcResult::Error {
+                error: Error::DIV,
+                origin: cell,
+                message: "division by 0".to_string(),
+            };
+        }
+        CalcResult::Number(total / count)
+    }
+
+    pub(crate) fn fn_minifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        let mut min = f64::INFINITY;
+        let apply_min = |value: f64| min = value.min(min);
+        if let Err(e) = self.apply_ifs(args, cell, apply_min) {
+            return e;
+        }
+
+        if min.is_infinite() {
+            min = 0.0;
+        }
+        CalcResult::Number(min)
+    }
+
+    pub(crate) fn fn_maxifs(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        let mut max = -f64::INFINITY;
+        let apply_max = |value: f64| max = value.max(max);
+        if let Err(e) = self.apply_ifs(args, cell, apply_max) {
+            return e;
+        }
+        if max.is_infinite() {
+            max = 0.0;
+        }
+        CalcResult::Number(max)
+    }
+}

base/src/functions/statistical/log_normal.rs

@@ -0,0 +1,124 @@
+use statrs::distribution::{Continuous, ContinuousCDF, LogNormal};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_log_norm_dist(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let mean = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        // Excel domain checks
+        if x <= 0.0 || std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for LOGNORM.DIST".to_string(),
+            };
+        }
+
+        let dist = match LogNormal::new(mean, std_dev) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameter for LOGNORM.DIST".to_string(),
+                }
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for LOGNORM.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    pub(crate) fn fn_log_norm_inv(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        use statrs::distribution::{ContinuousCDF, LogNormal};
+
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let mean = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // Excel domain checks
+        if p <= 0.0 || p >= 1.0 || std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for LOGNORM.INV".to_string(),
+            };
+        }
+
+        let dist = match LogNormal::new(mean, std_dev) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameter for LOGNORM.INV".to_string(),
+                }
+            }
+        };
+
+        let result = dist.inverse_cdf(p);
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameter for LOGNORM.INV".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+}

base/src/functions/statistical/mod.rs

@@ -0,0 +1,23 @@
+mod beta;
+mod binom;
+mod chisq;
+mod count_and_average;
+mod covariance;
+mod devsq;
+mod exponential;
+mod fisher;
+mod gamma;
+mod geomean;
+mod hypegeom;
+mod if_ifs;
+mod log_normal;
+mod normal;
+mod pearson;
+mod phi;
+mod poisson;
+mod standard_dev;
+mod standardize;
+mod t_dist;
+mod variance;
+mod weibull;
+mod z_test;

base/src/functions/statistical/normal.rs

@@ -0,0 +1,325 @@
+use statrs::distribution::{Continuous, ContinuousCDF, Normal, StudentsT};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // NORM.DIST(x, mean, standard_dev, cumulative)
+    pub(crate) fn fn_norm_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let mean = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        // Excel: standard_dev must be > 0
+        if std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "standard_dev must be > 0 in NORM.DIST".to_string(),
+            };
+        }
+
+        let dist = match Normal::new(mean, std_dev) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for NORM.DIST".to_string(),
+                }
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for NORM.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // NORM.INV(probability, mean, standard_dev)
+    pub(crate) fn fn_norm_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let mean = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        if p <= 0.0 || p >= 1.0 || std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for NORM.INV".to_string(),
+            };
+        }
+
+        let dist = match Normal::new(mean, std_dev) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for NORM.INV".to_string(),
+                }
+            }
+        };
+
+        let x = dist.inverse_cdf(p);
+
+        if !x.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for NORM.INV".to_string(),
+            };
+        }
+
+        CalcResult::Number(x)
+    }
+
+    // NORM.S.DIST(z, cumulative)
+    pub(crate) fn fn_norm_s_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let z = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let cumulative = match self.get_boolean(&args[1], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        let dist = match Normal::new(0.0, 1.0) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::ERROR,
+                    origin: cell,
+                    message: "Failed to construct standard normal distribution".to_string(),
+                }
+            }
+        };
+
+        let result = if cumulative { dist.cdf(z) } else { dist.pdf(z) };
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for NORM.S.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // NORM.S.INV(probability)
+    pub(crate) fn fn_norm_s_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 1 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        if p <= 0.0 || p >= 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "probability must be in (0,1) in NORM.S.INV".to_string(),
+            };
+        }
+
+        let dist = match Normal::new(0.0, 1.0) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::ERROR,
+                    origin: cell,
+                    message: "Failed to construct standard normal distribution".to_string(),
+                }
+            }
+        };
+
+        let z = dist.inverse_cdf(p);
+
+        if !z.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for NORM.S.INV".to_string(),
+            };
+        }
+
+        CalcResult::Number(z)
+    }
+
+    pub(crate) fn fn_confidence_norm(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let alpha = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let size = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.floor(),
+            Err(e) => return e,
+        };
+
+        if alpha <= 0.0 || alpha >= 1.0 || std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for CONFIDENCE.NORM".to_string(),
+            };
+        }
+        if size < 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Sample size must be at least 1".to_string(),
+            };
+        }
+
+        let normal = match Normal::new(0.0, 1.0) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::ERROR,
+                    cell,
+                    "Failed to construct normal distribution".to_string(),
+                )
+            }
+        };
+
+        let quantile = normal.inverse_cdf(1.0 - alpha / 2.0);
+        if !quantile.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid quantile for CONFIDENCE.NORM".to_string(),
+            };
+        }
+
+        let margin = quantile * std_dev / size.sqrt();
+        CalcResult::Number(margin)
+    }
+
+    pub(crate) fn fn_confidence_t(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let alpha = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let size = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // Domain checks
+        if alpha <= 0.0 || alpha >= 1.0 || std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for CONFIDENCE.T".to_string(),
+            };
+        }
+
+        // Need at least 2 observations so df = n - 1 > 0
+        if size < 2.0 {
+            return CalcResult::Error {
+                error: Error::DIV,
+                origin: cell,
+                message: "Sample size must be at least 2".to_string(),
+            };
+        }
+
+        let df = size - 1.0;
+
+        let t_dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::ERROR,
+                    cell,
+                    "Failed to construct Student's t distribution".to_string(),
+                )
+            }
+        };
+
+        // Two-sided CI => use 1 - alpha/2
+        let t_crit = t_dist.inverse_cdf(1.0 - alpha / 2.0);
+        if !t_crit.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid quantile for CONFIDENCE.T".to_string(),
+            };
+        }
+
+        let margin = t_crit * std_dev / size.sqrt();
+        CalcResult::Number(margin)
+    }
+}

base/src/functions/statistical/pearson.rs

@@ -0,0 +1,235 @@
+use crate::expressions::types::CellReferenceIndex;
+use crate::functions::statistical::chisq::is_same_shape_or_1d;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // PEARSON(array1, array2)
+    pub(crate) fn fn_pearson(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let left_arg = self.evaluate_node_in_context(&args[0], cell);
+        let right_arg = self.evaluate_node_in_context(&args[1], cell);
+
+        let (values_left, values_right) = match (left_arg, right_arg) {
+            (
+                CalcResult::Range {
+                    left: l1,
+                    right: r1,
+                },
+                CalcResult::Range {
+                    left: l2,
+                    right: r2,
+                },
+            ) => {
+                if l1.sheet != l2.sheet {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Ranges are in different sheets".to_string(),
+                    );
+                }
+
+                let rows1 = r1.row - l1.row + 1;
+                let cols1 = r1.column - l1.column + 1;
+                let rows2 = r2.row - l2.row + 1;
+                let cols2 = r2.column - l2.column + 1;
+
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Ranges must be of the same shape".to_string(),
+                    );
+                }
+
+                let values_left = match self.values_from_range(l1, r1) {
+                    Err(error) => return error,
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_range(l2, r2) {
+                    Err(error) => return error,
+                    Ok(v) => v,
+                };
+
+                (values_left, values_right)
+            }
+            (
+                CalcResult::Array(left),
+                CalcResult::Range {
+                    left: l2,
+                    right: r2,
+                },
+            ) => {
+                let rows2 = r2.row - l2.row + 1;
+                let cols2 = r2.column - l2.column + 1;
+
+                let rows1 = left.len() as i32;
+                let cols1 = if rows1 > 0 { left[0].len() as i32 } else { 0 };
+
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Array and range must be of the same shape".to_string(),
+                    );
+                }
+
+                let values_left = match self.values_from_array(left) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in first array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_range(l2, r2) {
+                    Err(error) => return error,
+                    Ok(v) => v,
+                };
+
+                (values_left, values_right)
+            }
+            (
+                CalcResult::Range {
+                    left: l1,
+                    right: r1,
+                },
+                CalcResult::Array(right),
+            ) => {
+                let rows1 = r1.row - l1.row + 1;
+                let cols1 = r1.column - l1.column + 1;
+
+                let rows2 = right.len() as i32;
+                let cols2 = if rows2 > 0 { right[0].len() as i32 } else { 0 };
+
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Range and array must be of the same shape".to_string(),
+                    );
+                }
+
+                let values_left = match self.values_from_range(l1, r1) {
+                    Err(error) => return error,
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_array(right) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in second array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+
+                (values_left, values_right)
+            }
+            (CalcResult::Array(left), CalcResult::Array(right)) => {
+                let rows1 = left.len() as i32;
+                let rows2 = right.len() as i32;
+                let cols1 = if rows1 > 0 { left[0].len() as i32 } else { 0 };
+                let cols2 = if rows2 > 0 { right[0].len() as i32 } else { 0 };
+
+                if !is_same_shape_or_1d(rows1, cols1, rows2, cols2) {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Arrays must be of the same shape".to_string(),
+                    );
+                }
+
+                let values_left = match self.values_from_array(left) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in first array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+                let values_right = match self.values_from_array(right) {
+                    Err(error) => {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            format!("Error in second array: {:?}", error),
+                        );
+                    }
+                    Ok(v) => v,
+                };
+
+                (values_left, values_right)
+            }
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Both arguments must be ranges or arrays".to_string(),
+                );
+            }
+        };
+
+        // Flatten into (x, y) pairs, skipping non-numeric entries (None)
+        let mut n: f64 = 0.0;
+        let mut sum_x = 0.0;
+        let mut sum_y = 0.0;
+        let mut sum_x2 = 0.0;
+        let mut sum_y2 = 0.0;
+        let mut sum_xy = 0.0;
+
+        let len = values_left.len().min(values_right.len());
+        for i in 0..len {
+            match (values_left[i], values_right[i]) {
+                (Some(x), Some(y)) => {
+                    n += 1.0;
+                    sum_x += x;
+                    sum_y += y;
+                    sum_x2 += x * x;
+                    sum_y2 += y * y;
+                    sum_xy += x * y;
+                }
+                _ => {
+                    // Ignore pairs where at least one side is non-numeric
+                }
+            }
+        }
+
+        if n < 2.0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "PEARSON requires at least two numeric pairs".to_string(),
+            );
+        }
+
+        // Pearson correlation:
+        // r = [ n*Σxy - (Σx)(Σy) ] / sqrt( [n*Σx² - (Σx)²] [n*Σy² - (Σy)²] )
+        let num = n * sum_xy - sum_x * sum_y;
+        let denom_x = n * sum_x2 - sum_x * sum_x;
+        let denom_y = n * sum_y2 - sum_y * sum_y;
+
+        if denom_x.abs() < 1e-15 || denom_y.abs() < 1e-15 {
+            // Zero variance in at least one series
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "PEARSON cannot be computed when one series has zero variance".to_string(),
+            );
+        }
+
+        let denom = (denom_x * denom_y).sqrt();
+        let r = num / denom;
+
+        CalcResult::Number(r)
+    }
+}

base/src/functions/statistical/phi.rs

@@ -0,0 +1,21 @@
+use crate::expressions::types::CellReferenceIndex;
+use crate::{calc_result::CalcResult, expressions::parser::Node, model::Model};
+
+impl Model {
+    // PHI(x) = standard normal PDF at x
+    pub(crate) fn fn_phi(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 1 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        // Standard normal PDF: (1 / sqrt(2π)) * exp(-x^2 / 2)
+        let result = (-(x * x) / 2.0).exp() / (2.0 * std::f64::consts::PI).sqrt();
+
+        CalcResult::Number(result)
+    }
+}

base/src/functions/statistical/poisson.rs

@@ -0,0 +1,94 @@
+use statrs::distribution::{Discrete, DiscreteCDF, Poisson};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // =POISSON.DIST(x, mean, cumulative)
+    pub(crate) fn fn_poisson_dist(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        // x
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        // mean (lambda)
+        let lambda = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[2], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if x < 0.0 || lambda < 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for POISSON.DIST".to_string(),
+            };
+        }
+
+        // Guard against insane k for u64
+        if x < 0.0 || x > (u64::MAX as f64) {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for POISSON.DIST".to_string(),
+            };
+        }
+
+        let k = x as u64;
+
+        // Special-case lambda = 0: degenerate distribution at 0
+        if lambda == 0.0 {
+            let result = if cumulative {
+                // For x >= 0, P(X <= x) = 1
+                1.0
+            } else {
+                // P(X = 0) = 1, P(X = k>0) = 0
+                if k == 0 {
+                    1.0
+                } else {
+                    0.0
+                }
+            };
+            return CalcResult::Number(result);
+        }
+
+        let dist = match Poisson::new(lambda) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for POISSON.DIST".to_string(),
+                }
+            }
+        };
+
+        let prob = if cumulative { dist.cdf(k) } else { dist.pmf(k) };
+
+        if !prob.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for POISSON.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(prob)
+    }
+}

base/src/functions/statistical/standard_dev.rs

@@ -0,0 +1,519 @@
+use crate::constants::{LAST_COLUMN, LAST_ROW};
+use crate::expressions::parser::ArrayNode;
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_stdev_p(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric
+                }
+            }
+        }
+
+        if count == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "STDEV.P with no numeric data".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / n;
+
+        // clamp tiny negatives from FP noise
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var.sqrt())
+    }
+
+    pub(crate) fn fn_stdev_s(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric
+                }
+            }
+        }
+
+        if count <= 1 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "STDEV.S requires at least two numeric values".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / (n - 1.0);
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var.sqrt())
+    }
+
+    pub(crate) fn fn_stdeva(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                CalcResult::String(_) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, 0.0);
+                                }
+                                CalcResult::Boolean(value) => {
+                                    let val = if value { 1.0 } else { 0.0 };
+                                    accumulate(&mut sum, &mut sumsq, &mut count, val);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric for now
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric for now
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric for now
+                }
+            }
+        }
+
+        if count <= 1 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "STDEVA requires at least two numeric values".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / (n - 1.0);
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var.sqrt())
+    }
+
+    pub(crate) fn fn_stdevpa(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                CalcResult::String(_) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, 0.0);
+                                }
+                                CalcResult::Boolean(value) => {
+                                    let val = if value { 1.0 } else { 0.0 };
+                                    accumulate(&mut sum, &mut sumsq, &mut count, val);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric for now
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric for now
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric for now
+                }
+            }
+        }
+
+        if count == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "STDEVPA with no numeric data".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / n;
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var.sqrt())
+    }
+}

base/src/functions/statistical/standardize.rs

@@ -0,0 +1,38 @@
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_standardize(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        // STANDARDIZE(x, mean, standard_dev)
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let mean = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+        let std_dev = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        if std_dev <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "standard_dev must be > 0 in STANDARDIZE".to_string(),
+            };
+        }
+
+        let z = (x - mean) / std_dev;
+
+        CalcResult::Number(z)
+    }
+}

base/src/functions/statistical/t_dist.rs

@@ -0,0 +1,576 @@
+use statrs::distribution::{Continuous, ContinuousCDF, StudentsT};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+fn mean(xs: &[f64]) -> f64 {
+    let n = xs.len();
+    if n == 0 {
+        return 0.0;
+    }
+    let mut s = 0.0;
+    for &x in xs {
+        s += x;
+    }
+    s / (n as f64)
+}
+
+fn sample_var(xs: &[f64]) -> f64 {
+    let n = xs.len();
+    if n < 2 {
+        return 0.0;
+    }
+    let m = mean(xs);
+    let mut s = 0.0;
+    for &x in xs {
+        let d = x - m;
+        s += d * d;
+    }
+    s / ((n - 1) as f64)
+}
+
+enum TTestType {
+    Paired,
+    TwoSampleEqualVar,
+    TwoSampleUnequalVar,
+}
+
+enum TTestTails {
+    OneTailed,
+    TwoTailed,
+}
+
+impl Model {
+    // T.DIST(x, deg_freedom, cumulative)
+    pub(crate) fn fn_t_dist(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[2], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if df < 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "deg_freedom must be >= 1 in T.DIST".to_string(),
+            };
+        }
+
+        let dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for T.DIST".to_string(),
+                }
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for T.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // T.DIST.2T(x, deg_freedom)
+    pub(crate) fn fn_t_dist_2t(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        if x < 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "x must be >= 0 in T.DIST.2T".to_string(),
+            };
+        }
+
+        if df < 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "deg_freedom must be >= 1 in T.DIST.2T".to_string(),
+            };
+        }
+
+        let dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for T.DIST.2T".to_string(),
+                }
+            }
+        };
+
+        let upper_tail = 1.0 - dist.cdf(x);
+        let mut result = 2.0 * upper_tail;
+
+        result = result.clamp(0.0, 1.0);
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for T.DIST.2T".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // T.DIST.RT(x, deg_freedom)
+    pub(crate) fn fn_t_dist_rt(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        if df < 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "deg_freedom must be >= 1 in T.DIST.RT".to_string(),
+            };
+        }
+
+        let dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for T.DIST.RT".to_string(),
+                }
+            }
+        };
+
+        let result = 1.0 - dist.cdf(x);
+
+        if !result.is_finite() || result < 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for T.DIST.RT".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+
+    // T.INV(probability, deg_freedom)
+    pub(crate) fn fn_t_inv(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        if p <= 0.0 || p >= 1.0 || df < 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for T.INV".to_string(),
+            };
+        }
+
+        let dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for T.INV".to_string(),
+                }
+            }
+        };
+
+        let x = dist.inverse_cdf(p);
+
+        if !x.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for T.INV".to_string(),
+            };
+        }
+
+        CalcResult::Number(x)
+    }
+
+    // T.INV.2T(probability, deg_freedom)
+    pub(crate) fn fn_t_inv_2t(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 2 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let p = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let df = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f.trunc(),
+            Err(e) => return e,
+        };
+
+        if p <= 0.0 || p > 1.0 || df < 1.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for T.INV.2T".to_string(),
+            };
+        }
+
+        let dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for T.INV.2T".to_string(),
+                }
+            }
+        };
+
+        // Two-sided: F(x) = 1 - p/2
+        let target_cdf = 1.0 - p / 2.0;
+        let x = dist.inverse_cdf(target_cdf);
+
+        if !x.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for T.INV.2T".to_string(),
+            };
+        }
+
+        CalcResult::Number(x.abs())
+    }
+
+    // T.TEST(array1, array2, tails, type)
+    pub(crate) fn fn_t_test(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let values1_opts = match self.evaluate_node_in_context(&args[0], cell) {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(a) => match self.values_from_array(a) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in first array: {:?}", error),
+                    );
+                }
+            },
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "First argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        let values2_opts = match self.evaluate_node_in_context(&args[1], cell) {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(a) => match self.values_from_array(a) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in second array: {:?}", error),
+                    );
+                }
+            },
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Second argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        let tails = match self.get_number(&args[2], cell) {
+            Ok(f) => {
+                let tf = f.trunc();
+                if tf == 1.0 {
+                    TTestTails::OneTailed
+                } else if tf == 2.0 {
+                    TTestTails::TwoTailed
+                } else {
+                    return CalcResult::new_error(
+                        Error::NUM,
+                        cell,
+                        "tails must be 1 or 2".to_string(),
+                    );
+                }
+            }
+            Err(e) => return e,
+        };
+        let test_type = match self.get_number(&args[3], cell) {
+            Ok(f) => {
+                let tf = f.trunc();
+                match tf {
+                    1.0 => TTestType::Paired,
+                    2.0 => TTestType::TwoSampleEqualVar,
+                    3.0 => TTestType::TwoSampleUnequalVar,
+                    _ => {
+                        return CalcResult::new_error(
+                            Error::NUM,
+                            cell,
+                            "type must be 1, 2, or 3".to_string(),
+                        );
+                    }
+                }
+            }
+            Err(e) => return e,
+        };
+
+        // keep only numeric entries, ignore non-numeric (Option::None)
+        let values1: Vec<f64> = values1_opts.into_iter().flatten().collect();
+        let values2: Vec<f64> = values2_opts.into_iter().flatten().collect();
+
+        let n1 = values1.len();
+        let n2 = values2.len();
+
+        if n1 == 0 || n2 == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "T.TEST requires non-empty samples".to_string(),
+            );
+        }
+
+        let (t_stat, df) = match test_type {
+            TTestType::Paired => {
+                if n1 != n2 {
+                    return CalcResult::new_error(
+                        Error::NA,
+                        cell,
+                        "For paired T.TEST, both samples must have the same length".to_string(),
+                    );
+                }
+                if n1 < 2 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Paired T.TEST requires at least two pairs".to_string(),
+                    );
+                }
+
+                let mut diffs = Vec::with_capacity(n1);
+                for i in 0..n1 {
+                    diffs.push(values1[i] - values2[i]);
+                }
+
+                let nd = diffs.len();
+                let md = mean(&diffs);
+                let vd = sample_var(&diffs);
+                if vd <= 0.0 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Zero variance in paired T.TEST".to_string(),
+                    );
+                }
+                let sd = vd.sqrt();
+                let t_stat = md / (sd / (nd as f64).sqrt());
+                let df = (nd - 1) as f64;
+                (t_stat, df)
+            }
+
+            // 2: two-sample, equal variance (homoscedastic)
+            TTestType::TwoSampleEqualVar => {
+                if n1 < 2 || n2 < 2 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Two-sample T.TEST type 2 requires at least two values in each sample"
+                            .to_string(),
+                    );
+                }
+
+                let m1 = mean(&values1);
+                let m2 = mean(&values2);
+                let v1 = sample_var(&values1);
+                let v2 = sample_var(&values2);
+
+                let df_i = (n1 + n2 - 2) as i32;
+                if df_i <= 0 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Degrees of freedom must be positive in T.TEST type 2".to_string(),
+                    );
+                }
+                let df = df_i as f64;
+
+                let sp2 = (((n1 - 1) as f64) * v1 + ((n2 - 1) as f64) * v2) / df; // pooled variance
+
+                if sp2 <= 0.0 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Zero pooled variance in T.TEST type 2".to_string(),
+                    );
+                }
+
+                let denom = (sp2 * (1.0 / (n1 as f64) + 1.0 / (n2 as f64))).sqrt();
+                if denom == 0.0 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Zero denominator in T.TEST type 2".to_string(),
+                    );
+                }
+
+                let t_stat = (m1 - m2) / denom;
+                (t_stat, df)
+            }
+
+            // two-sample, unequal variance (Welch)
+            TTestType::TwoSampleUnequalVar => {
+                if n1 < 2 || n2 < 2 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Two-sample T.TEST type 3 requires at least two values in each sample"
+                            .to_string(),
+                    );
+                }
+
+                let m1 = mean(&values1);
+                let m2 = mean(&values2);
+                let v1 = sample_var(&values1);
+                let v2 = sample_var(&values2);
+
+                let s1n = v1 / (n1 as f64);
+                let s2n = v2 / (n2 as f64);
+                let denom = (s1n + s2n).sqrt();
+                if denom == 0.0 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Zero denominator in T.TEST type 3".to_string(),
+                    );
+                }
+
+                let t_stat = (m1 - m2) / denom;
+
+                let num_df = (s1n + s2n).powi(2);
+                let den_df = (s1n * s1n) / ((n1 - 1) as f64) + (s2n * s2n) / ((n2 - 1) as f64);
+                if den_df == 0.0 {
+                    return CalcResult::new_error(
+                        Error::DIV,
+                        cell,
+                        "Invalid degrees of freedom in T.TEST type 3".to_string(),
+                    );
+                }
+                let df = num_df / den_df;
+                (t_stat, df)
+            }
+        };
+
+        if df <= 0.0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "Degrees of freedom must be positive in T.TEST".to_string(),
+            );
+        }
+
+        let dist = match StudentsT::new(0.0, 1.0, df) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Invalid parameters for Student's t distribution".to_string(),
+                );
+            }
+        };
+
+        let t_abs = t_stat.abs();
+        let cdf = dist.cdf(t_abs);
+
+        let mut p = match tails {
+            TTestTails::OneTailed => 1.0 - cdf,
+            TTestTails::TwoTailed => 2.0 * (1.0 - cdf),
+        };
+
+        // clamp tiny fp noise
+        if p < 0.0 && p > -1e-15 {
+            p = 0.0;
+        }
+        if p > 1.0 && p < 1.0 + 1e-15 {
+            p = 1.0;
+        }
+
+        CalcResult::Number(p)
+    }
+}

base/src/functions/statistical/variance.rs

@@ -0,0 +1,518 @@
+use crate::constants::{LAST_COLUMN, LAST_ROW};
+use crate::expressions::parser::ArrayNode;
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    pub(crate) fn fn_var_p(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric
+                }
+            }
+        }
+
+        if count == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "VAR.P with no numeric data".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / n;
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var)
+    }
+
+    pub(crate) fn fn_var_s(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric
+                }
+            }
+        }
+
+        if count <= 1 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "VAR.S requires at least two numeric values".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / (n - 1.0);
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var)
+    }
+
+    pub(crate) fn fn_vara(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..=row2 {
+                        for column in column1..=column2 {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                CalcResult::String(_) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, 0.0);
+                                }
+                                CalcResult::Boolean(value) => {
+                                    let val = if value { 1.0 } else { 0.0 };
+                                    accumulate(&mut sum, &mut sumsq, &mut count, val);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric for now (A semantics to be added)
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric for now (A semantics to be added)
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric for now (A semantics to be added)
+                }
+            }
+        }
+
+        if count <= 1 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "VARA requires at least two numeric values".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / (n - 1.0);
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var)
+    }
+
+    pub(crate) fn fn_varpa(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        if args.is_empty() {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let mut sum = 0.0;
+        let mut sumsq = 0.0;
+        let mut count: u64 = 0;
+
+        #[inline]
+        fn accumulate(sum: &mut f64, sumsq: &mut f64, count: &mut u64, value: f64) {
+            *sum += value;
+            *sumsq += value * value;
+            *count += 1;
+        }
+
+        for arg in args {
+            match self.evaluate_node_in_context(arg, cell) {
+                CalcResult::Number(value) => {
+                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                }
+                CalcResult::Range { left, right } => {
+                    if left.sheet != right.sheet {
+                        return CalcResult::new_error(
+                            Error::VALUE,
+                            cell,
+                            "Ranges are in different sheets".to_string(),
+                        );
+                    }
+
+                    let row1 = left.row;
+                    let mut row2 = right.row;
+                    let column1 = left.column;
+                    let mut column2 = right.column;
+
+                    if row1 == 1 && row2 == LAST_ROW {
+                        row2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_row,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+                    if column1 == 1 && column2 == LAST_COLUMN {
+                        column2 = match self.workbook.worksheet(left.sheet) {
+                            Ok(s) => s.dimension().max_column,
+                            Err(_) => {
+                                return CalcResult::new_error(
+                                    Error::ERROR,
+                                    cell,
+                                    format!("Invalid worksheet index: '{}'", left.sheet),
+                                );
+                            }
+                        };
+                    }
+
+                    for row in row1..row2 + 1 {
+                        for column in column1..(column2 + 1) {
+                            match self.evaluate_cell(CellReferenceIndex {
+                                sheet: left.sheet,
+                                row,
+                                column,
+                            }) {
+                                CalcResult::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                CalcResult::String(_) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, 0.0);
+                                }
+                                CalcResult::Boolean(value) => {
+                                    let val = if value { 1.0 } else { 0.0 };
+                                    accumulate(&mut sum, &mut sumsq, &mut count, val);
+                                }
+                                error @ CalcResult::Error { .. } => return error,
+                                _ => {
+                                    // ignore non-numeric for now
+                                }
+                            }
+                        }
+                    }
+                }
+                CalcResult::Array(array) => {
+                    for row in array {
+                        for value in row {
+                            match value {
+                                ArrayNode::Number(value) => {
+                                    accumulate(&mut sum, &mut sumsq, &mut count, value);
+                                }
+                                ArrayNode::Error(error) => {
+                                    return CalcResult::Error {
+                                        error,
+                                        origin: cell,
+                                        message: "Error in array".to_string(),
+                                    }
+                                }
+                                _ => {
+                                    // ignore non-numeric for now
+                                }
+                            }
+                        }
+                    }
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    // ignore non-numeric for now
+                }
+            }
+        }
+
+        if count == 0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "VARPA with no numeric data".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mut var = (sumsq - (sum * sum) / n) / n;
+
+        if var < 0.0 && var > -1e-12 {
+            var = 0.0;
+        }
+
+        CalcResult::Number(var)
+    }
+}

base/src/functions/statistical/weibull.rs

@@ -0,0 +1,71 @@
+use statrs::distribution::{Continuous, ContinuousCDF, Weibull};
+
+use crate::expressions::types::CellReferenceIndex;
+use crate::{
+    calc_result::CalcResult, expressions::parser::Node, expressions::token::Error, model::Model,
+};
+
+impl Model {
+    // WEIBULL.DIST(x, alpha, beta, cumulative)
+    pub(crate) fn fn_weibull_dist(
+        &mut self,
+        args: &[Node],
+        cell: CellReferenceIndex,
+    ) -> CalcResult {
+        if args.len() != 4 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let x = match self.get_number_no_bools(&args[0], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let alpha = match self.get_number_no_bools(&args[1], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let beta = match self.get_number_no_bools(&args[2], cell) {
+            Ok(f) => f,
+            Err(e) => return e,
+        };
+
+        let cumulative = match self.get_boolean(&args[3], cell) {
+            Ok(b) => b,
+            Err(e) => return e,
+        };
+
+        if x < 0.0 || alpha <= 0.0 || beta <= 0.0 {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid parameters for WEIBULL.DIST".to_string(),
+            };
+        }
+
+        // statrs::Weibull: shape = k (alpha), scale = lambda (beta)
+        let dist = match Weibull::new(alpha, beta) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::Error {
+                    error: Error::NUM,
+                    origin: cell,
+                    message: "Invalid parameters for WEIBULL.DIST".to_string(),
+                }
+            }
+        };
+
+        let result = if cumulative { dist.cdf(x) } else { dist.pdf(x) };
+
+        if !result.is_finite() {
+            return CalcResult::Error {
+                error: Error::NUM,
+                origin: cell,
+                message: "Invalid result for WEIBULL.DIST".to_string(),
+            };
+        }
+
+        CalcResult::Number(result)
+    }
+}

base/src/functions/statistical/z_test.rs

@@ -0,0 +1,171 @@
+use statrs::distribution::{ContinuousCDF, Normal};
+
+use crate::expressions::token::Error;
+use crate::expressions::types::CellReferenceIndex;
+use crate::{calc_result::CalcResult, expressions::parser::Node, model::Model};
+
+impl Model {
+    // Z.TEST(array, x, [sigma])
+    pub(crate) fn fn_z_test(&mut self, args: &[Node], cell: CellReferenceIndex) -> CalcResult {
+        // 2 or 3 arguments
+        if args.len() < 2 || args.len() > 3 {
+            return CalcResult::new_args_number_error(cell);
+        }
+
+        let array_arg = self.evaluate_node_in_context(&args[0], cell);
+
+        // Flatten first argument into Vec<Option<f64>> (numeric / non-numeric)
+        let values = match array_arg {
+            CalcResult::Range { left, right } => match self.values_from_range(left, right) {
+                Ok(v) => v,
+                Err(error) => return error,
+            },
+            CalcResult::Array(array) => match self.values_from_array(array) {
+                Ok(v) => v,
+                Err(error) => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        format!("Error in array argument: {:?}", error),
+                    );
+                }
+            },
+            CalcResult::Number(v) => vec![Some(v)],
+            error @ CalcResult::Error { .. } => return error,
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Z.TEST first argument must be a range or array".to_string(),
+                );
+            }
+        };
+
+        // Collect basic stats on numeric entries
+        let mut sum = 0.0;
+        let mut count: u64 = 0;
+
+        for x in values.iter().flatten() {
+            sum += x;
+            count += 1;
+        }
+
+        // Excel: if array has no numeric values -> #N/A
+        if count == 0 {
+            return CalcResult::new_error(
+                Error::NA,
+                cell,
+                "Z.TEST array has no numeric data".to_string(),
+            );
+        }
+
+        let n = count as f64;
+        let mean = sum / n;
+
+        // x argument (hypothesized population mean)
+        let x_value = match self.evaluate_node_in_context(&args[1], cell) {
+            CalcResult::Number(v) => v,
+            error @ CalcResult::Error { .. } => return error,
+            _ => {
+                return CalcResult::new_error(
+                    Error::VALUE,
+                    cell,
+                    "Z.TEST second argument (x) must be numeric".to_string(),
+                );
+            }
+        };
+
+        // Optional sigma
+        let mut sigma: Option<f64> = None;
+        if args.len() == 3 {
+            match self.evaluate_node_in_context(&args[2], cell) {
+                CalcResult::Number(v) => {
+                    if v == 0.0 {
+                        return CalcResult::new_error(
+                            Error::NUM,
+                            cell,
+                            "Z.TEST sigma cannot be zero".to_string(),
+                        );
+                    }
+                    sigma = Some(v);
+                }
+                error @ CalcResult::Error { .. } => return error,
+                _ => {
+                    return CalcResult::new_error(
+                        Error::VALUE,
+                        cell,
+                        "Z.TEST sigma (third argument) must be numeric".to_string(),
+                    );
+                }
+            }
+        }
+
+        // If sigma omitted, use sample standard deviation STDEV(array)
+        let sigma_value = if let Some(s) = sigma {
+            s
+        } else {
+            // Excel: if only one numeric value and sigma omitted -> #DIV/0!
+            if count <= 1 {
+                return CalcResult::new_error(
+                    Error::DIV,
+                    cell,
+                    "Z.TEST requires at least two values when sigma is omitted".to_string(),
+                );
+            }
+
+            // Compute sum of squared deviations
+            let mut sumsq_dev = 0.0;
+            for x in values.iter().flatten() {
+                let d = x - mean;
+                sumsq_dev += d * d;
+            }
+
+            let var = sumsq_dev / (n - 1.0);
+            if var <= 0.0 {
+                return CalcResult::new_error(
+                    Error::DIV,
+                    cell,
+                    "Z.TEST standard deviation is zero".to_string(),
+                );
+            }
+
+            var.sqrt()
+        };
+
+        // Compute z statistic: (mean - x) / (sigma / sqrt(n))
+        let denom = sigma_value / n.sqrt();
+        if denom == 0.0 {
+            return CalcResult::new_error(
+                Error::DIV,
+                cell,
+                "Z.TEST denominator is zero".to_string(),
+            );
+        }
+
+        let z = (mean - x_value) / denom;
+
+        // Standard normal CDF
+        let dist = match Normal::new(0.0, 1.0) {
+            Ok(d) => d,
+            Err(_) => {
+                return CalcResult::new_error(
+                    Error::NUM,
+                    cell,
+                    "Cannot create standard normal distribution in Z.TEST".to_string(),
+                );
+            }
+        };
+
+        let mut p = 1.0 - dist.cdf(z);
+
+        // clamp tiny FP noise
+        if p < 0.0 && p > -1e-15 {
+            p = 0.0;
+        }
+        if p > 1.0 && p < 1.0 + 1e-15 {
+            p = 1.0;
+        }
+
+        CalcResult::Number(p)
+    }
+}

base/src/test/mod.rs

@@ -55,12 +55,17 @@ mod test_yearfrac_basis;
 pub(crate) mod util;
 
 mod engineering;
+mod statistical;
 mod test_fn_offset;
 mod test_number_format;
 
 mod test_arrays;
+mod test_combin_combina;
 mod test_escape_quotes;
+mod test_even_odd;
+mod test_exp_sign;
 mod test_extend;
+mod test_fn_datevalue_timevalue;
 mod test_fn_fv;
 mod test_fn_round;
 mod test_fn_type;
@@ -80,5 +85,6 @@ mod test_percentage;
 mod test_set_functions_error_handling;
 mod test_sheet_names;
 mod test_today;
+mod test_trigonometric_reciprocals;
 mod test_types;
 mod user_model;

base/src/test/statistical/mod.rs

@@ -0,0 +1,22 @@
+mod test_fn_avedev;
+mod test_fn_binom;
+mod test_fn_chisq;
+mod test_fn_chisq_test;
+mod test_fn_confidence;
+mod test_fn_covariance;
+mod test_fn_devsq;
+mod test_fn_expon_dist;
+mod test_fn_f;
+mod test_fn_fisher;
+mod test_fn_hyp_geom_dist;
+mod test_fn_log_norm;
+mod test_fn_norm_dist;
+mod test_fn_pearson;
+mod test_fn_phi;
+mod test_fn_poisson;
+mod test_fn_stdev;
+mod test_fn_t_dist;
+mod test_fn_t_test;
+mod test_fn_var;
+mod test_fn_weibull;
+mod test_fn_z_test;

base/src/test/statistical/test_fn_avedev.rs

@@ -0,0 +1,40 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn smoke_test() {
+    let mut model = new_empty_model();
+    model._set("A1", "=STDEV.P(10, 12, 23, 23, 16, 23, 21)");
+    model._set("A2", "=STDEV.S(10, 12, 23, 23, 16, 23, 21)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"5.174505793");
+
+    assert_eq!(model._get_text("A2"), *"5.589105048");
+}
+
+#[test]
+fn numbers() {
+    let mut model = new_empty_model();
+
+    model._set("A2", "24");
+    model._set("A3", "25");
+    model._set("A4", "27");
+    model._set("A5", "23");
+    model._set("A6", "45");
+    model._set("A7", "23.5");
+    model._set("A8", "34");
+    model._set("A9", "23");
+    model._set("A10", "23");
+    model._set("A11", "TRUE");
+    model._set("A12", "'23");
+    model._set("A13", "Text");
+    model._set("A14", "FALSE");
+    model._set("A15", "45");
+
+    model._set("B1", "=AVEDEV(A2:A15)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("B1"), *"7.25");
+}

base/src/test/statistical/test_fn_binom.rs

@@ -0,0 +1,86 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_binom_dist_smoke() {
+    let mut model = new_empty_model();
+    model._set("A1", "=BINOM.DIST(6, 10, 0.5, TRUE)");
+    model._set("A2", "=BINOM.DIST(6, 10, 0.5, FALSE)");
+    model._set("A3", "=BINOM.DIST(6, 10, 0.5)"); // wrong args
+    model._set("A4", "=BINOM.DIST(6, 10, 0.5, TRUE, FALSE)"); // too many args
+    model.evaluate();
+
+    // P(X <= 6) for X ~ Bin(10, 0.5) = 0.828125
+    assert_eq!(model._get_text("A1"), *"0.828125");
+
+    // P(X = 6) for X ~ Bin(10, 0.5) = 0.205078125
+    assert_eq!(model._get_text("A2"), *"0.205078125");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+}
+
+#[test]
+fn test_fn_binom_dist_range_smoke() {
+    let mut model = new_empty_model();
+    model._set("A1", "=BINOM.DIST.RANGE(60, 0.75, 48)");
+    model._set("A2", "=BINOM.DIST.RANGE(60, 0.75, 45, 50)");
+    model._set("A3", "=BINOM.DIST.RANGE(60, 1.2, 45, 50)"); // p > 1 -> #NUM!
+    model._set("A4", "=BINOM.DIST.RANGE(60, 0.75, 50, 45)"); // lower > upper -> #NUM!");
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.083974967");
+
+    assert_eq!(model._get_text("A2"), *"0.523629793");
+
+    assert_eq!(model._get_text("A3"), *"#NUM!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_binom_inv_smoke() {
+    let mut model = new_empty_model();
+    model._set("A1", "=BINOM.INV(6, 0.5, 0.75)");
+    model._set("A2", "=BINOM.INV(6, 0.5, -0.1)"); // alpha < 0 -> #NUM!
+    model._set("A3", "=BINOM.INV(6, 1.2, 0.75)"); // p > 1 -> #NUM!
+    model._set("A4", "=BINOM.INV(6, 0.5)"); // args error
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"4");
+    assert_eq!(model._get_text("A2"), *"#NUM!");
+    assert_eq!(model._get_text("A3"), *"#NUM!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+}
+
+#[test]
+fn test_fn_negbinom_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: PMF (non-cumulative) and CDF (cumulative)
+    model._set("A1", "=NEGBINOM.DIST(10, 5, 0.25, FALSE)");
+    model._set("A2", "=NEGBINOM.DIST(10, 5, 0.25, TRUE)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=NEGBINOM.DIST(10, 5, 0.25)");
+    model._set("A4", "=NEGBINOM.DIST(10, 5, 0.25, TRUE, FALSE)");
+
+    // Domain errors:
+    // p < 0 or p > 1 -> #NUM!
+    model._set("A5", "=NEGBINOM.DIST(10, 5, 1.5, TRUE)");
+    // number_f < 0 -> #NUM!
+    model._set("A6", "=NEGBINOM.DIST(-1, 5, 0.25, TRUE)");
+    // number_s < 1 -> #NUM!
+    model._set("A7", "=NEGBINOM.DIST(10, 0, 0.25, TRUE)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.05504866");
+    assert_eq!(model._get_text("A2"), *"0.313514058");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+}

base/src/test/statistical/test_fn_chisq.rs

@@ -0,0 +1,140 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_chisq_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: CDF
+    model._set("A1", "=CHISQ.DIST(0.5, 4, TRUE)");
+
+    // Valid: PDF
+    model._set("A2", "=CHISQ.DIST(0.5, 4, FALSE)");
+
+    // Valid: CDF with numeric cumulative (1 -> TRUE)
+    model._set("A3", "=CHISQ.DIST(0.5, 4, 1)");
+
+    // Wrong number of args -> #ERROR!
+    model._set("A4", "=CHISQ.DIST(0.5, 4)");
+    model._set("A5", "=CHISQ.DIST(0.5, 4, TRUE, FALSE)");
+
+    // Domain errors
+    // x < 0 -> #NUM!
+    model._set("A6", "=CHISQ.DIST(-1, 4, TRUE)");
+    // deg_freedom < 1 -> #NUM!
+    model._set("A7", "=CHISQ.DIST(0.5, 0, TRUE)");
+
+    model.evaluate();
+
+    // Values for df = 4
+    // CDF(0.5) ≈ 0.026499021, PDF(0.5) ≈ 0.097350098
+    assert_eq!(model._get_text("A1"), *"0.026499021");
+    assert_eq!(model._get_text("A2"), *"0.097350098");
+    assert_eq!(model._get_text("A3"), *"0.026499021");
+
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#ERROR!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_chisq_dist_rt_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid calls
+    model._set("A1", "=CHISQ.DIST.RT(0.5, 4)");
+    model._set("A2", "=CHISQ.DIST.RT(5, 4)");
+
+    // Too few / too many args -> #ERROR!
+    model._set("A3", "=CHISQ.DIST.RT(0.5)");
+    model._set("A4", "=CHISQ.DIST.RT(0.5, 4, 1)");
+
+    // Domain errors
+    // x < 0 -> #NUM!
+    model._set("A5", "=CHISQ.DIST.RT(-1, 4)");
+    // deg_freedom < 1 -> #NUM!
+    model._set("A6", "=CHISQ.DIST.RT(0.5, 0)");
+
+    model.evaluate();
+
+    // For df = 4:
+    // right tail at 0.5 ≈ 0.973500979
+    // right tail at 5.0 ≈ 0.287297495
+    assert_eq!(model._get_text("A1"), *"0.973500979");
+    assert_eq!(model._get_text("A2"), *"0.287297495");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_chisq_inv_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid calls
+    model._set("A1", "=CHISQ.INV(0.95, 4)");
+    model._set("A2", "=CHISQ.INV(0.1, 10)");
+
+    // Wrong number of args -> #ERROR!
+    model._set("A3", "=CHISQ.INV(0.95)");
+    model._set("A4", "=CHISQ.INV(0.95, 4, 1)");
+
+    // Domain errors
+    // probability < 0 or > 1 -> #NUM!
+    model._set("A5", "=CHISQ.INV(-0.1, 4)");
+    model._set("A6", "=CHISQ.INV(1.1, 4)");
+    // deg_freedom < 1 -> #NUM!
+    model._set("A7", "=CHISQ.INV(0.5, 0)");
+
+    model.evaluate();
+
+    // Standard critical values:
+    // CHISQ.INV(0.95, 4) ≈ 9.487729037
+    // CHISQ.INV(0.1, 10) ≈ 4.865182052
+    assert_eq!(model._get_text("A1"), *"9.487729037");
+    assert_eq!(model._get_text("A2"), *"4.865182052");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_chisq_inv_rt_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid calls
+    model._set("A1", "=CHISQ.INV.RT(0.05, 4)");
+    model._set("A2", "=CHISQ.INV.RT(0.9, 10)");
+
+    // Wrong number of args -> #ERROR!
+    model._set("A3", "=CHISQ.INV.RT(0.05)");
+    model._set("A4", "=CHISQ.INV.RT(0.05, 4, 1)");
+
+    // Domain errors
+    // probability < 0 or > 1 -> #NUM!
+    model._set("A5", "=CHISQ.INV.RT(-0.1, 4)");
+    model._set("A6", "=CHISQ.INV.RT(1.1, 4)");
+    // deg_freedom < 1 -> #NUM!
+    model._set("A7", "=CHISQ.INV.RT(0.5, 0)");
+
+    model.evaluate();
+
+    // For chi-square:
+    // CHISQ.INV.RT(0.05, 4) = CHISQ.INV(0.95, 4) ≈ 9.487729037
+    // CHISQ.INV.RT(0.9, 10)  = CHISQ.INV(0.1, 10) ≈ 4.865182052
+    assert_eq!(model._get_text("A1"), *"9.487729037");
+    assert_eq!(model._get_text("A2"), *"4.865182052");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+}

base/src/test/statistical/test_fn_chisq_test.rs

@@ -0,0 +1,127 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_chisq_test_smoke() {
+    let mut model = new_empty_model();
+    model._set("A2", "48");
+    model._set("A3", "32");
+    model._set("A4", "12");
+    model._set("A5", "1");
+    model._set("A6", "'13");
+    model._set("A7", "TRUE");
+    model._set("A8", "1");
+    model._set("A9", "13");
+    model._set("A10", "15");
+
+    model._set("B2", "55");
+    model._set("B3", "34");
+    model._set("B4", "13");
+    model._set("B5", "blah");
+    model._set("B6", "13");
+    model._set("B7", "1");
+    model._set("B8", "TRUE");
+    model._set("B9", "'14");
+    model._set("B10", "16");
+
+    model._set("C1", "=CHISQ.TEST(A2:A10, B2:B10)");
+    model.evaluate();
+    assert_eq!(model._get_text("C1"), *"0.997129538");
+}
+
+#[test]
+fn arrays() {
+    let mut model = new_empty_model();
+    model._set("A2", "TRUE");
+    model._set("A3", "4");
+    model._set("A4", "'3");
+    model._set("B2", "2");
+    model._set("B3", "2");
+    model._set("B4", "2");
+    model._set("C1", "=CHISQ.TEST(A2:A4, B2:B4)");
+
+    model._set("G5", "=CHISQ.TEST({TRUE,4,\"3\"}, {2,2,2})");
+
+    // 1D arrays with different shapes
+    model._set("G6", "=CHISQ.TEST({1,2,3}, {3;3;4})");
+
+    // 2D array
+    model._set("G7", "=CHISQ.TEST({1,2;3,4},{2,3;2,2})");
+
+    // 1D arrays with same shape
+    model._set("G8", "=CHISQ.TEST({1,2,3,4}, {2,3,4,5})");
+
+    model.evaluate();
+    assert_eq!(model._get_text("C1"), *"0.367879441");
+    assert_eq!(model._get_text("G5"), *"0.367879441");
+
+    assert_eq!(model._get_text("G6"), *"0.383531573");
+
+    assert_eq!(model._get_text("G7"), *"0.067889155");
+
+    assert_eq!(model._get_text("G8"), *"0.733094495");
+}
+
+#[test]
+fn more_arrays() {
+    let mut model = new_empty_model();
+    model._set("V20", "2");
+    model._set("V21", "4");
+    model._set("W20", "3");
+    model._set("W21", "5");
+    model._set("C1", "=CHISQ.TEST({1,2;3,4},V20:W21)");
+    model._set("C2", "=CHISQ.TEST({1,2;3,4}, {2,3;4,5})");
+    model.evaluate();
+    assert_eq!(model._get_text("C1"), *"0.257280177");
+    assert_eq!(model._get_text("C2"), *"0.257280177");
+}
+
+#[test]
+fn array_ranges() {
+    let mut model = new_empty_model();
+    model._set("A2", "TRUE");
+    model._set("A3", "4");
+    model._set("A4", "'3");
+    model._set("B2", "2");
+    model._set("B3", "2");
+    model._set("B4", "2");
+    model._set("C1", "=CHISQ.TEST(A2:A4, {2;2;2})");
+
+    model._set("G5", "=CHISQ.TEST({TRUE;4;\"3\"}, B2:B4)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("C1"), *"0.367879441");
+    assert_eq!(model._get_text("G5"), *"0.367879441");
+}
+
+#[test]
+fn array_2d_ranges() {
+    let mut model = new_empty_model();
+    model._set("A2", "2");
+    model._set("B2", "3");
+    model._set("C2", "4");
+    model._set("A3", "5");
+    model._set("B3", "6");
+    model._set("C3", "7");
+    model._set("G1", "=CHISQ.TEST({1,2,3;4,2,6}, A2:C3)");
+    model.evaluate();
+    assert_eq!(model._get_text("G1"), *"0.129195493");
+}
+
+#[test]
+fn ranges_1d() {
+    let mut model = new_empty_model();
+    model._set("A2", "1");
+    model._set("A3", "2");
+    model._set("A4", "3");
+    model._set("B2", "4");
+    model._set("C2", "5");
+    model._set("D2", "6");
+    model._set("G1", "=CHISQ.TEST(A2:A4, B2:D2)");
+    model._set("G2", "=CHISQ.TEST(B2:D2, A2:A4)");
+
+    model.evaluate();
+    assert_eq!(model._get_text("G1"), *"0.062349477");
+    assert_eq!(model._get_text("G2"), *"0.000261259");
+}

base/src/test/statistical/test_fn_confidence.rs

@@ -0,0 +1,51 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_confidence_norm_smoke() {
+    let mut model = new_empty_model();
+
+    model._set("A1", "=CONFIDENCE.NORM(0.05, 2.5, 50)");
+
+    // Some edge/error cases
+    model._set("A2", "=CONFIDENCE.NORM(0, 2.5, 50)"); // alpha <= 0  -> #NUM!
+    model._set("A3", "=CONFIDENCE.NORM(1, 2.5, 50)"); // alpha >= 1  -> #NUM!
+    model._set("A4", "=CONFIDENCE.NORM(0.05, -1, 50)"); // std_dev <=0 -> #NUM!
+    model._set("A5", "=CONFIDENCE.NORM(0.05, 2.5, 1)");
+    model._set("A6", "=CONFIDENCE.NORM(0.05, 2.5, 0.99)"); // size < 1 -> #NUM!
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.692951912");
+
+    assert_eq!(model._get_text("A2"), *"#NUM!");
+    assert_eq!(model._get_text("A3"), *"#NUM!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"4.899909961");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_confidence_t_smoke() {
+    let mut model = new_empty_model();
+
+    model._set("A1", "=CONFIDENCE.T(0.05, 50000, 100)");
+
+    // Some edge/error cases
+    model._set("A2", "=CONFIDENCE.T(0, 50000, 100)"); // alpha <= 0 -> #NUM!
+    model._set("A3", "=CONFIDENCE.T(1, 50000, 100)"); // alpha >= 1 -> #NUM!
+    model._set("A4", "=CONFIDENCE.T(0.05, -1, 100)");
+    model._set("A5", "=CONFIDENCE.T(0.05, 50000, 1)");
+    model._set("A6", "=CONFIDENCE.T(0.05, 50000, 1.7)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"9921.08475793");
+
+    assert_eq!(model._get_text("A2"), *"#NUM!");
+    assert_eq!(model._get_text("A3"), *"#NUM!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"#DIV/0!");
+    assert_eq!(model._get_text("A6"), *"#DIV/0!");
+}

base/src/test/statistical/test_fn_covariance.rs

@@ -0,0 +1,57 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_covariance_smoke() {
+    let mut model = new_empty_model();
+    model._set("A1", "3");
+    model._set("A2", "9");
+    model._set("A3", "2");
+    model._set("A4", "7");
+    model._set("A5", "4");
+    model._set("A6", "12");
+
+    model._set("B1", "5");
+    model._set("B2", "15");
+    model._set("B3", "6");
+    model._set("B4", "17");
+    model._set("B5", "8");
+    model._set("B6", "20");
+
+    model._set("C1", "=COVARIANCE.P(A1:A6, B1:B6)");
+    model._set("C2", "=COVARIANCE.S(A1:A6, B1:B6)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("C1"), *"19.194444444");
+    assert_eq!(model._get_text("C2"), *"23.033333333");
+}
+
+#[test]
+fn arrays_mixed() {
+    let mut model = new_empty_model();
+
+    model._set("A2", "2");
+    model._set("A3", "4");
+    model._set("A4", "6");
+    model._set("A5", "8");
+
+    model._set("B2", "1");
+    model._set("B3", "3");
+    model._set("B4", "5");
+    model._set("B5", "7");
+
+    model._set("C1", "=COVARIANCE.P(A2:A5, {1,3,5,7})");
+    model._set("C2", "=COVARIANCE.S(A2:A5, {1,3,5,7})");
+    model._set("C3", "=COVARIANCE.P(A2:A5, B2:B5)");
+    model._set("C4", "=COVARIANCE.S(A2:A5, B2:B5)");
+    model._set("C5", "=COVARIANCE.P({2,4,6,8}, B2:B5)");
+    model._set("C6", "=COVARIANCE.S({2,4,6,8}, B2:B5)");
+    model._set("C7", "=COVARIANCE.P({2,4,6,8}, {1,3,5,7})");
+    model._set("C8", "=COVARIANCE.S({2,4,6,8}, {1,3,5,7})");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("C1"), *"5");
+    assert_eq!(model._get_text("C2"), *"6.666666667");
+}

base/src/test/statistical/test_fn_devsq.rs

@@ -0,0 +1,50 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn arguments_smoke_test() {
+    let mut model = new_empty_model();
+    model._set("A1", "=DEVSQ()");
+    model._set("A2", "=DEVSQ(1, 2, 3)");
+    model._set("A3", "=DEVSQ(1, )");
+    model._set("A4", "=DEVSQ(1,   , 3)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"#ERROR!");
+    assert_eq!(model._get_text("A2"), *"2");
+    assert_eq!(model._get_text("A3"), *"0");
+    assert_eq!(model._get_text("A4"), *"2");
+}
+
+#[test]
+fn ranges() {
+    let mut model = new_empty_model();
+    model._set("A1", "=DEVSQ(A2:A8)");
+    model._set("A2", "4");
+    model._set("A3", "5");
+    model._set("A4", "8");
+    model._set("A5", "7");
+    model._set("A6", "11");
+    model._set("A7", "4");
+    model._set("A8", "3");
+    model.evaluate();
+    assert_eq!(model._get_text("A1"), *"48");
+}
+
+#[test]
+fn arrays() {
+    let mut model = new_empty_model();
+    model._set("A1", "=DEVSQ({1, 2, 3})");
+    model._set("A2", "=DEVSQ({1; 2; 3})");
+    model._set("A3", "=DEVSQ({1, 2; 3, 4})");
+    model._set("A4", "=DEVSQ({1, 2; 3, 4; 5, 6})");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"2");
+    assert_eq!(model._get_text("A2"), *"2");
+    assert_eq!(model._get_text("A3"), *"5");
+    assert_eq!(model._get_text("A4"), *"17.5");
+}

base/src/test/statistical/test_fn_expon_dist.rs

@@ -0,0 +1,32 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_expon_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // λ = 1, x = 0.5
+    // CDF = 1 - e^-0.5 ≈ 0.393469340
+    // PDF = e^-0.5 ≈ 0.606530660
+    model._set("A1", "=EXPON.DIST(0.5, 1, TRUE)");
+    model._set("A2", "=EXPON.DIST(0.5, 1, FALSE)");
+
+    // Wrong number of args
+    model._set("A3", "=EXPON.DIST(0.5, 1)");
+    model._set("A4", "=EXPON.DIST(0.5, 1, TRUE, FALSE)");
+
+    // Domain errors
+    model._set("A5", "=EXPON.DIST(-1, 1, TRUE)"); // x < 0
+    model._set("A6", "=EXPON.DIST(0.5, 0, TRUE)"); // lambda <= 0
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.39346934");
+    assert_eq!(model._get_text("A2"), *"0.60653066");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}

base/src/test/statistical/test_fn_f.rs

@@ -0,0 +1,75 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_f_dist_sanity() {
+    let mut model = new_empty_model();
+    model._set("A1", "=F.DIST(15, 6, 4, TRUE)");
+    model._set("A2", "=F.DIST(15, 6, 4, FALSE)");
+    model._set("A3", "=F.DIST(15, 6, 4)");
+    model._set("A4", "=F.DIST(15, 6, 4, TRUE, FALSE)");
+    model.evaluate();
+    assert_eq!(model._get_text("A1"), *"0.989741952");
+    assert_eq!(model._get_text("A2"), *"0.001271447");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+}
+
+#[test]
+fn test_fn_f_dist_rt_sanity() {
+    let mut model = new_empty_model();
+
+    // Valid call
+    model._set("A1", "=F.DIST.RT(15, 6, 4)");
+    // Too few args
+    model._set("A2", "=F.DIST.RT(15, 6)");
+    // Too many args
+    model._set("A3", "=F.DIST.RT(15, 6, 4, 1)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.010258048");
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+}
+
+#[test]
+fn test_fn_f_inv_sanity() {
+    let mut model = new_empty_model();
+
+    // Valid call: left-tail inverse
+    model._set("A1", "=F.INV(0.9897419523940, 6, 4)");
+
+    // Too many args
+    model._set("A2", "=F.INV(0.5, 6, 4, 2)");
+
+    // Too few args
+    model._set("A3", "=F.INV(0.5, 6)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"15");
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+}
+
+#[test]
+fn test_fn_f_inv_rt_sanity() {
+    let mut model = new_empty_model();
+
+    // Valid call: left-tail inverse
+    model._set("A1", "=F.INV.RT(0.0102580476059808, 6, 4)");
+
+    // Too many args
+    model._set("A2", "=F.INV.RT(0.5, 6, 4, 2)");
+
+    // Too few args
+    model._set("A3", "=F.INV.RT(0.5, 6)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"15");
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+}

base/src/test/statistical/test_fn_fisher.rs

@@ -0,0 +1,53 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+#[test]
+fn test_fn_fisher_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid inputs
+    model._set("A1", "=FISHER(0.1)");
+    model._set("A2", "=FISHER(-0.5)");
+    model._set("A3", "=FISHER(0.8)");
+
+    // Domain errors: x <= -1 or x >= 1 -> #NUM!
+    model._set("A4", "=FISHER(1)");
+    model._set("A5", "=FISHER(-1)");
+    model._set("A6", "=FISHER(2)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A7", "=FISHER(0.1, 2)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.100335348");
+    assert_eq!(model._get_text("A2"), *"-0.549306144");
+    assert_eq!(model._get_text("A3"), *"1.098612289");
+
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+
+    assert_eq!(model._get_text("A7"), *"#ERROR!");
+}
+
+#[test]
+fn test_fn_fisher_inv_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid inputs
+    model._set("A1", "=FISHERINV(-1.5)");
+    model._set("A2", "=FISHERINV(0.5)");
+    model._set("A3", "=FISHERINV(2)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A4", "=FISHERINV(0.5, 1)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"-0.905148254");
+    assert_eq!(model._get_text("A2"), *"0.462117157");
+    assert_eq!(model._get_text("A3"), *"0.96402758");
+
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+}

base/src/test/statistical/test_fn_hyp_geom_dist.rs

@@ -0,0 +1,42 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_hyp_geom_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: PDF (non-cumulative)
+    model._set("A1", "=HYPGEOM.DIST(1, 4, 12, 20, FALSE)");
+
+    // Valid: CDF (cumulative)
+    model._set("A2", "=HYPGEOM.DIST(1, 4, 12, 20, TRUE)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=HYPGEOM.DIST(1, 4, 12, 20)");
+    model._set("A4", "=HYPGEOM.DIST(1, 4, 12, 20, TRUE, FALSE)");
+
+    // Domain errors:
+    // sample_s > number_sample -> #NUM!
+    model._set("A5", "=HYPGEOM.DIST(5, 4, 12, 20, TRUE)");
+
+    // population_s > number_pop -> #NUM!
+    model._set("A6", "=HYPGEOM.DIST(1, 4, 25, 20, TRUE)");
+
+    // number_sample > number_pop -> #NUM!
+    model._set("A7", "=HYPGEOM.DIST(1, 25, 12, 20, TRUE)");
+
+    model.evaluate();
+
+    // PDF: P(X = 1)
+    assert_eq!(model._get_text("A1"), *"0.13869969");
+
+    // CDF: P(X <= 1)
+    assert_eq!(model._get_text("A2"), *"0.153147575");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+}

base/src/test/statistical/test_fn_log_norm.rs

@@ -0,0 +1,61 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_log_norm_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: CDF and PDF
+    model._set("A1", "=LOGNORM.DIST(4, 3.5, 1.2, TRUE)");
+    model._set("A2", "=LOGNORM.DIST(4, 3.5, 1.2, FALSE)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=LOGNORM.DIST(4, 3.5, 1.2)");
+    model._set("A4", "=LOGNORM.DIST(4, 3.5, 1.2, TRUE, FALSE)");
+
+    // Domain errors:
+    // x <= 0 -> #NUM!
+    model._set("A5", "=LOGNORM.DIST(0, 3.5, 1.2, TRUE)");
+    // std_dev <= 0 -> #NUM!
+    model._set("A6", "=LOGNORM.DIST(4, 3.5, 0, TRUE)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.039083556");
+    assert_eq!(model._get_text("A2"), *"0.017617597");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_log_norm_inv_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid call
+    model._set("A1", "=LOGNORM.INV(0.5, 3.5, 1.2)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A2", "=LOGNORM.INV(0.5, 3.5)");
+    model._set("A3", "=LOGNORM.INV(0.5, 3.5, 1.2, 0)");
+
+    // Domain errors:
+    // probability <= 0 or >= 1 -> #NUM!
+    model._set("A4", "=LOGNORM.INV(0, 3.5, 1.2)");
+    model._set("A5", "=LOGNORM.INV(1, 3.5, 1.2)");
+    // std_dev <= 0 -> #NUM!
+    model._set("A6", "=LOGNORM.INV(0.5, 3.5, 0)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"33.115451959");
+
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}

base/src/test/statistical/test_fn_norm_dist.rs

@@ -0,0 +1,119 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_norm_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: standard normal as a special case
+    model._set("A1", "=NORM.DIST(1, 0, 1, TRUE)");
+    model._set("A2", "=NORM.DIST(1, 0, 1, FALSE)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=NORM.DIST(1, 0, 1)");
+    model._set("A4", "=NORM.DIST(1, 0, 1, TRUE, FALSE)");
+
+    // Domain errors: standard_dev <= 0 -> #NUM!
+    model._set("A5", "=NORM.DIST(1, 0, 0, TRUE)");
+    model._set("A6", "=NORM.DIST(1, 0, -1, TRUE)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.841344746");
+    assert_eq!(model._get_text("A2"), *"0.241970725");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_norm_inv_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: median of standard normal
+    model._set("A1", "=NORM.INV(0.5, 0, 1)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A2", "=NORM.INV(0.5, 0)");
+    model._set("A3", "=NORM.INV(0.5, 0, 1, 0)");
+
+    // Domain errors:
+    // probability <= 0 or >= 1 -> #NUM!
+    model._set("A4", "=NORM.INV(0, 0, 1)");
+    model._set("A5", "=NORM.INV(1, 0, 1)");
+    // standard_dev <= 0 -> #NUM!
+    model._set("A6", "=NORM.INV(0.5, 0, 0)");
+
+    model._set("A7", "=NORM.INV(0.7, 0.2, 1)");
+    model._set("A8", "=NORM.INV(0.7, 0.2, 5)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0");
+
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"0.724400513");
+    assert_eq!(model._get_text("A8"), *"2.822002564");
+}
+
+#[test]
+fn test_fn_norm_s_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: CDF and PDF at z = 0
+    model._set("A1", "=NORM.S.DIST(0, TRUE)");
+    model._set("A2", "=NORM.S.DIST(0, FALSE)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=NORM.S.DIST(0)");
+    model._set("A4", "=NORM.S.DIST(0, TRUE, FALSE)");
+
+    model._set("A5", "=NORM.S.DIST(0.2, FALSE)");
+    model._set("A6", "=NORM.S.DIST(2.2, TRUE)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.5");
+    assert_eq!(model._get_text("A2"), *"0.39894228");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+
+    assert_eq!(model._get_text("A5"), *"0.391042694");
+    assert_eq!(model._get_text("A6"), *"0.986096552");
+}
+
+#[test]
+fn test_fn_norm_s_inv_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: symmetric points
+    model._set("A1", "=NORM.S.INV(0.5)");
+    model._set("A2", "=NORM.S.INV(0.841344746)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=NORM.S.INV()");
+    model._set("A4", "=NORM.S.INV(0.5, 0)");
+
+    // Domain errors: probability <= 0 or >= 1 -> #NUM!
+    model._set("A5", "=NORM.S.INV(0)");
+    model._set("A6", "=NORM.S.INV(1)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0");
+    // Approximately 1
+    assert_eq!(model._get_text("A2"), *"1");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}

base/src/test/statistical/test_fn_pearson.rs

@@ -0,0 +1,31 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_chisq_test_smoke() {
+    let mut model = new_empty_model();
+    model._set("A2", "48");
+    model._set("A3", "32");
+    model._set("A4", "12");
+    model._set("A5", "1");
+    model._set("A6", "'13");
+    model._set("A7", "TRUE");
+    model._set("A8", "1");
+    model._set("A9", "13");
+    model._set("A10", "15");
+
+    model._set("B2", "55");
+    model._set("B3", "34");
+    model._set("B4", "13");
+    model._set("B5", "blah");
+    model._set("B6", "13");
+    model._set("B7", "1");
+    model._set("B8", "TRUE");
+    model._set("B9", "'14");
+    model._set("B10", "16");
+
+    model._set("C1", "=PEARSON(A2:A10, B2:B10)");
+    model.evaluate();
+    assert_eq!(model._get_text("C1"), *"0.998381439");
+}

base/src/test/statistical/test_fn_phi.rs

@@ -0,0 +1,26 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_phi_smoke() {
+    let mut model = new_empty_model();
+
+    model._set("A1", "=PHI(0)");
+    model._set("A2", "=PHI(1)");
+    model._set("A3", "=PHI(-1)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A4", "=PHI()");
+    model._set("A5", "=PHI(0, 1)");
+
+    model.evaluate();
+
+    // Standard values
+    assert_eq!(model._get_text("A1"), *"0.39894228");
+    assert_eq!(model._get_text("A2"), *"0.241970725");
+    assert_eq!(model._get_text("A3"), *"0.241970725");
+
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#ERROR!");
+}

base/src/test/statistical/test_fn_poisson.rs

@@ -0,0 +1,41 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_poisson_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // λ = 2, x = 3
+    // P(X = 3)  ≈ 0.180447045
+    // P(X <= 3) ≈ 0.857123461
+    model._set("A1", "=POISSON.DIST(3, 2, FALSE)");
+    model._set("A2", "=POISSON.DIST(3, 2, TRUE)");
+
+    // Wrong arg count
+    model._set("A3", "=POISSON.DIST(3, 2)");
+    model._set("A4", "=POISSON.DIST(3, 2, TRUE, FALSE)");
+
+    // Domain errors
+    model._set("A5", "=POISSON.DIST(-1, 2, TRUE)"); // x < 0
+    model._set("A6", "=POISSON.DIST(3, -2, TRUE)"); // mean < 0
+
+    // λ = 0 special cases
+    model._set("A7", "=POISSON.DIST(0, 0, FALSE)"); // 1
+    model._set("A8", "=POISSON.DIST(1, 0, FALSE)"); // 0
+    model._set("A9", "=POISSON.DIST(5, 0, TRUE)"); // 1
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.180447044");
+    assert_eq!(model._get_text("A2"), *"0.85712346");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+
+    assert_eq!(model._get_text("A7"), *"1");
+    assert_eq!(model._get_text("A8"), *"0");
+    assert_eq!(model._get_text("A9"), *"1");
+}

base/src/test/statistical/test_fn_stdev.rs

@@ -0,0 +1,46 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn smoke_test() {
+    let mut model = new_empty_model();
+    model._set("A1", "=STDEV.P(10, 12, 23, 23, 16, 23, 21)");
+    model._set("A2", "=STDEV.S(10, 12, 23, 23, 16, 23, 21)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"5.174505793");
+
+    assert_eq!(model._get_text("A2"), *"5.589105048");
+}
+
+#[test]
+fn numbers() {
+    let mut model = new_empty_model();
+
+    model._set("A2", "24");
+    model._set("A3", "25");
+    model._set("A4", "27");
+    model._set("A5", "23");
+    model._set("A6", "45");
+    model._set("A7", "23.5");
+    model._set("A8", "34");
+    model._set("A9", "23");
+    model._set("A10", "23");
+    model._set("A11", "TRUE");
+    model._set("A12", "'23");
+    model._set("A13", "Text");
+    model._set("A14", "FALSE");
+    model._set("A15", "45");
+
+    model._set("B1", "=STDEV.P(A2:A15)");
+    model._set("B2", "=STDEV.S(A2:A15)");
+    model._set("B3", "=STDEVA(A2:A15)");
+    model._set("B4", "=STDEVPA(A2:A15)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("B1"), *"8.483071378");
+    assert_eq!(model._get_text("B2"), *"8.941942369");
+    assert_eq!(model._get_text("B3"), *"15.499955689");
+    assert_eq!(model._get_text("B4"), *"14.936131032");
+}

base/src/test/statistical/test_fn_t_dist.rs

@@ -0,0 +1,160 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_t_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: cumulative (left-tail CDF)
+    model._set("A1", "=T.DIST(2, 10, TRUE)");
+    // Valid: probability density function (PDF)
+    model._set("B1", "=T.DIST(2, 10, FALSE)");
+
+    // Wrong number of arguments
+    model._set("A2", "=T.DIST(2, 10)");
+    model._set("A3", "=T.DIST(2, 10, TRUE, FALSE)");
+
+    // Domain error: df < 1 -> #NUM!
+    model._set("A4", "=T.DIST(2, 0, TRUE)");
+    model._set("A5", "=T.DIST(2, -1, TRUE)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.963305983");
+    assert_eq!(model._get_text("B1"), *"0.061145766");
+
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_t_dist_rt_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: right tail probability
+    model._set("A1", "=T.DIST.RT(2, 10)");
+
+    // Wrong number of arguments
+    model._set("A2", "=T.DIST.RT(2)");
+    model._set("A3", "=T.DIST.RT(2, 10, TRUE)");
+
+    // Domain error: df < 1
+    model._set("A4", "=T.DIST.RT(2, 0)");
+    model._set("A5", "=T.DIST.RT(2, -1)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.036694017");
+
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#NUM!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_t_dist_2t_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: two-tailed probability
+    model._set("A1", "=T.DIST.2T(2, 10)");
+
+    // In the limit case of x = 0, the two-tailed probability is 1.0
+    model._set("A4", "=T.DIST.2T(0, 10)");
+
+    // Wrong number of arguments
+    model._set("A2", "=T.DIST.2T(2)");
+    model._set("A3", "=T.DIST.2T(2, 10, TRUE)");
+
+    // Domain errors:
+    // x < 0 -> #NUM!
+    model._set("A5", "=T.DIST.2T(-0.001, 10)");
+    // df < 1 -> #NUM!
+    model._set("A6", "=T.DIST.2T(2, 0)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"0.073388035");
+    assert_eq!(model._get_text("A4"), *"1");
+
+    assert_eq!(model._get_text("A2"), *"#ERROR!");
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_t_inv_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: upper and lower tail
+    model._set("A1", "=T.INV(0.95, 10)");
+    model._set("A2", "=T.INV(0.05, 10)");
+    // limit case:
+    model._set("B2", "=T.INV(0.95, 1)");
+
+    // Wrong number of arguments
+    model._set("A3", "=T.INV(0.95)");
+    model._set("A4", "=T.INV(0.95, 10, 1)");
+
+    // Domain errors:
+    // p <= 0 or >= 1
+    model._set("A5", "=T.INV(0, 10)");
+    model._set("A6", "=T.INV(1, 10)");
+    // df < 1
+    model._set("A7", "=T.INV(0.95, 0)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"1.812461123");
+    assert_eq!(model._get_text("A2"), *"-1.812461123");
+    assert_eq!(model._get_text("B2"), *"6.313751515");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+}
+
+#[test]
+fn test_fn_t_inv_2t_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: two-tailed critical values
+    model._set("A1", "=T.INV.2T(0.1, 10)");
+    model._set("A2", "=T.INV.2T(0.05, 10)");
+
+    // p = 1 should give t = 0 (both tails outside are 1.0, so cut at the mean)
+    model._set("A3", "=T.INV.2T(1, 10)");
+
+    model._set("A7", "=T.INV.2T(1.5, 10)");
+
+    // Wrong number of arguments
+    model._set("A4", "=T.INV.2T(0.1)");
+    model._set("A5", "=T.INV.2T(0.1, 10, 1)");
+
+    // Domain errors:
+    // p <= 0 or p > 1
+    model._set("A6", "=T.INV.2T(0, 10)");
+    // df < 1
+    model._set("A8", "=T.INV.2T(0.1, 0)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"1.812461123");
+    assert_eq!(model._get_text("A2"), *"2.228138852");
+    assert_eq!(model._get_text("A3"), *"0");
+
+    // NB: Excel returns -0.699812061 for T.INV.2T(1.5, 10)
+    // which seems inconsistent with its documented behavior
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#ERROR!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A8"), *"#NUM!");
+}

base/src/test/statistical/test_fn_t_test.rs

@@ -0,0 +1,41 @@
+#![allow(clippy::unwrap_used)]
+use crate::test::util::new_empty_model;
+#[test]
+fn test_fn_t_test_smoke() {
+    let mut model = new_empty_model();
+    model._set("A2", "3");
+    model._set("A3", "4");
+    model._set("A4", "5");
+    model._set("A5", "6");
+    model._set("A6", "10");
+    model._set("A7", "3");
+    model._set("A8", "2");
+    model._set("A9", "4");
+    model._set("A10", "7");
+
+    model._set("B2", "6");
+    model._set("B3", "19");
+    model._set("B4", "3");
+    model._set("B5", "2");
+    model._set("B6", "13");
+    model._set("B7", "4");
+    model._set("B8", "5");
+    model._set("B9", "17");
+    model._set("B10", "3");
+
+    model._set("C1", "=T.TEST(A2:A10, B2:B10, 1, 1)");
+    model._set("C2", "=T.TEST(A2:A10, B2:B10, 1, 2)");
+    model._set("C3", "=T.TEST(A2:A10, B2:B10, 1, 3)");
+    model._set("C4", "=T.TEST(A2:A10, B2:B10, 2, 1)");
+    model._set("C5", "=T.TEST(A2:A10, B2:B10, 2, 2)");
+    model._set("C6", "=T.TEST(A2:A10, B2:B10, 2, 3)");
+
+    model.evaluate();
+
+    assert_eq!(model._get_text("C1"), *"0.103836888");
+    assert_eq!(model._get_text("C2"), *"0.100244599");
+    assert_eq!(model._get_text("C3"), *"0.105360319");
+    assert_eq!(model._get_text("C4"), *"0.207673777");
+    assert_eq!(model._get_text("C5"), *"0.200489197");
+    assert_eq!(model._get_text("C6"), *"0.210720639");
+}

base/src/test/statistical/test_fn_var.rs

@@ -0,0 +1,46 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn smoke_test() {
+    let mut model = new_empty_model();
+    model._set("A1", "=STDEV.P(10, 12, 23, 23, 16, 23, 21)");
+    model._set("A2", "=STDEV.S(10, 12, 23, 23, 16, 23, 21)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("A1"), *"5.174505793");
+
+    assert_eq!(model._get_text("A2"), *"5.589105048");
+}
+
+#[test]
+fn numbers() {
+    let mut model = new_empty_model();
+
+    model._set("A2", "24");
+    model._set("A3", "25");
+    model._set("A4", "27");
+    model._set("A5", "23");
+    model._set("A6", "45");
+    model._set("A7", "23.5");
+    model._set("A8", "34");
+    model._set("A9", "23");
+    model._set("A10", "23");
+    model._set("A11", "TRUE");
+    model._set("A12", "'23");
+    model._set("A13", "Text");
+    model._set("A14", "FALSE");
+    model._set("A15", "45");
+
+    model._set("B1", "=VAR.P(A2:A15)");
+    model._set("B2", "=VAR.S(A2:A15)");
+    model._set("B3", "=VARA(A2:A15)");
+    model._set("B4", "=VARPA(A2:A15)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("B1"), *"71.9625");
+    assert_eq!(model._get_text("B2"), *"79.958333333");
+    assert_eq!(model._get_text("B3"), *"240.248626374");
+    assert_eq!(model._get_text("B4"), *"223.088010204");
+}

base/src/test/statistical/test_fn_weibull.rs

@@ -0,0 +1,41 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_weibull_dist_smoke() {
+    let mut model = new_empty_model();
+
+    // Valid: CDF and PDF for x = 1, alpha = 2, beta = 1
+    model._set("A1", "=WEIBULL.DIST(1, 2, 1, TRUE)");
+    model._set("A2", "=WEIBULL.DIST(1, 2, 1, FALSE)");
+
+    // Wrong number of arguments -> #ERROR!
+    model._set("A3", "=WEIBULL.DIST(1, 2, 1)");
+    model._set("A4", "=WEIBULL.DIST(1, 2, 1, TRUE, FALSE)");
+
+    // Domain errors:
+    // x < 0  -> #NUM!
+    model._set("A5", "=WEIBULL.DIST(-1, 2, 1, TRUE)");
+    // alpha <= 0 -> #NUM!
+    model._set("A6", "=WEIBULL.DIST(1, 0, 1, TRUE)");
+    model._set("A7", "=WEIBULL.DIST(1, -1, 1, TRUE)");
+    // beta <= 0 -> #NUM!
+    model._set("A8", "=WEIBULL.DIST(1, 2, 0, TRUE)");
+    model._set("A9", "=WEIBULL.DIST(1, 2, -1, TRUE)");
+
+    model.evaluate();
+
+    // 1 - e^-1
+    assert_eq!(model._get_text("A1"), *"0.632120559");
+    // 2 * e^-1
+    assert_eq!(model._get_text("A2"), *"0.735758882");
+
+    assert_eq!(model._get_text("A3"), *"#ERROR!");
+    assert_eq!(model._get_text("A4"), *"#ERROR!");
+    assert_eq!(model._get_text("A5"), *"#NUM!");
+    assert_eq!(model._get_text("A6"), *"#NUM!");
+    assert_eq!(model._get_text("A7"), *"#NUM!");
+    assert_eq!(model._get_text("A8"), *"#NUM!");
+    assert_eq!(model._get_text("A9"), *"#NUM!");
+}

base/src/test/statistical/test_fn_z_test.rs

@@ -0,0 +1,36 @@
+#![allow(clippy::unwrap_used)]
+
+use crate::test::util::new_empty_model;
+
+#[test]
+fn test_fn_z_test_smoke() {
+    let mut model = new_empty_model();
+    model._set("A2", "3");
+    model._set("A3", "6");
+    model._set("A4", "7");
+    model._set("A5", "8");
+    model._set("A6", "6");
+    model._set("A7", "5");
+    model._set("A8", "4");
+    model._set("A9", "2");
+    model._set("A10", "1");
+    model._set("A11", "9");
+
+    model._set("G1", "=Z.TEST(A2:A11, 4)");
+    model._set("G2", "=Z.TEST(A2:A11, 6)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("G1"), *"0.090574197");
+    assert_eq!(model._get_text("G2"), *"0.863043389");
+}
+
+#[test]
+fn arrays() {
+    let mut model = new_empty_model();
+    model._set("D1", "=Z.TEST({5,2,3,4}, 4, 123)");
+    model._set("D2", "=Z.TEST({5,2,3,4}, 4)");
+    model.evaluate();
+
+    assert_eq!(model._get_text("D1"), *"0.503243397");
+    assert_eq!(model._get_text("D2"), *"0.780710987");
+}

base/src/test/test_combin_combina.rs

@@ -11,8 +11,8 @@ fn arguments() {
     model._set("A4", "=COMBINA()");
     model._set("A5", "=COMBIN(2)");
     model._set("A6", "=COMBINA(2)");
-    model._set("A5", "=COMBIN(1, 2, 3)");
+    model._set("A7", "=COMBIN(1, 2, 3)");
-    model._set("A6", "=COMBINA(1, 2, 3)");
+    model._set("A8", "=COMBINA(1, 2, 3)");
 
     model.evaluate();
 
@@ -24,4 +24,4 @@ fn arguments() {
     assert_eq!(model._get_text("A6"), *"#ERROR!");
     assert_eq!(model._get_text("A7"), *"#ERROR!");
     assert_eq!(model._get_text("A8"), *"#ERROR!");
-}
+}

base/src/test/test_fn_datevalue_timevalue.rs

@@ -7,8 +7,8 @@ fn datevalue_timevalue_arguments() {
     let mut model = new_empty_model();
     model._set("A1", "=DATEVALUE()");
     model._set("A2", "=TIMEVALUE()");
-    model._set("A3", "=DATEVALUE("2000-01-01")")
+    model._set("A3", "=DATEVALUE(\"2000-01-01\")");
-    model._set("A4", "=TIMEVALUE("12:00:00")")
+    model._set("A4", "=TIMEVALUE(\"12:00:00\")");
     model._set("A5", "=DATEVALUE(1,2)");
     model._set("A6", "=TIMEVALUE(1,2)");
     model.evaluate();
@@ -20,5 +20,3 @@ fn datevalue_timevalue_arguments() {
     assert_eq!(model._get_text("A5"), *"#ERROR!");
     assert_eq!(model._get_text("A6"), *"#ERROR!");
 }
-
-