fslaborg · kMutagene · Oct 21, 2025 · May 28, 2025 · Jun 23, 2025 · Jun 24, 2025
diff --git a/docs/BasicStats.fsx b/docs/BasicStats.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 (*** condition: ipynb ***)

diff --git a/docs/Clustering.fsx b/docs/Clustering.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/ComparisonMetrics.fsx b/docs/ComparisonMetrics.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"
@@ -27,7 +27,7 @@ Plotly.NET.Defaults.DefaultDisplayOptions <-
 #if IPYNB
 #r "nuget: Plotly.NET, 4.0.0"
 #r "nuget: Plotly.NET.Interactive, 4.0.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #r "nuget: FSharp.Stats"
 #endif // IPYNB
 
@@ -154,7 +154,7 @@ let mlcm =
                 [2; 0; 4]
             ]
             |> array2D
-            |> Matrix.Generic.ofArray2D
+            |> Matrix.ofArray2D
         )
     )
 (**

diff --git a/docs/Correlation.fsx b/docs/Correlation.fsx
@@ -14,11 +14,11 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 
 
 Plotly.NET.Defaults.DefaultDisplayOptions <-
@@ -29,7 +29,7 @@ Plotly.NET.Defaults.DefaultDisplayOptions <-
 #r "nuget: Plotly.NET, 4.0.0"
 #r "nuget: Plotly.NET.Interactive, 4.0.0"
 #r "nuget: FSharp.Stats"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #endif // IPYNB
 
 (** 

diff --git a/docs/Covariance.fsx b/docs/Covariance.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/CrossValidation.fsx b/docs/CrossValidation.fsx
@@ -15,7 +15,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"
@@ -357,7 +357,7 @@ let shuffleAndSplitPolynomial p iterations (xData: Vector<float>) (yData: Vector
    let getFitFuncPol xTrain yTrain (xTest:Vector<float>) = 
        getFitFuncPolynomial xTrain yTrain xTest order
 
-   CrossValidation.shuffelAndSplit p iterations xDataMat yData getFitFuncPol error Seq.stDev
+   CrossValidation.shuffleAndSplit p iterations xDataMat yData getFitFuncPol error Seq.stDev
 
 //creates an output for 5 iterations where random 20 % of the data set are taken as testing data set
 let sasPolynomial order = shuffleAndSplitPolynomial 0.2 5 xV yV order
@@ -368,7 +368,7 @@ let shuffleAndSplitSpline p iterations (xData: Vector<float>) (yData: Vector<flo
     let getFitFuncSpl xDat yDat (xDatTrain: Vector<float>) =
         getFitFuncSpline xDat yDat xDatTrain lambda
 
-    CrossValidation.shuffelAndSplit p iterations xDataMat yData getFitFuncSpl errorSpl Seq.stDev
+    CrossValidation.shuffleAndSplit p iterations xDataMat yData getFitFuncSpl errorSpl Seq.stDev
 
 //creates an output for 5 iterations where random 20 % of the data set are taken as testing data set
 let sasSpline lambda = shuffleAndSplitSpline 0.2 5 xV yV lambda

diff --git a/docs/Differentiation.fsx b/docs/Differentiation.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Distributions.fsx b/docs/Distributions.fsx
@@ -15,7 +15,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #r "nuget: Plotly.NET, 4.0.0"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"

diff --git a/docs/Fitting.fsx b/docs/Fitting.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/GoodnessOfFit.fsx b/docs/GoodnessOfFit.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/GrowthCurve.fsx b/docs/GrowthCurve.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Imputation.fsx b/docs/Imputation.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"
@@ -28,7 +28,7 @@ Plotly.NET.Defaults.DefaultDisplayOptions <-
 #r "nuget: Plotly.NET, 4.0.0"
 #r "nuget: Plotly.NET.Interactive, 4.0.0"
 #r "nuget: FSharp.Stats"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 open FsMath
 
 open Plotly.NET

diff --git a/docs/Integration.fsx b/docs/Integration.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Interpolation.fsx b/docs/Interpolation.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Intervals.fsx b/docs/Intervals.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Normalization.fsx b/docs/Normalization.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Optimization.fsx b/docs/Optimization.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Quantiles.fsx b/docs/Quantiles.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Rank.fsx b/docs/Rank.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Signal.fsx b/docs/Signal.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/Testing.fsx b/docs/Testing.fsx
@@ -14,7 +14,7 @@ categoryindex: 0
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/docs/index.fsx b/docs/index.fsx
@@ -5,7 +5,7 @@
 #r "nuget: FSharpAux, 2.0.0"
 #r "nuget: FSharpAux.IO, 2.0.0"
 #r "nuget: OptimizedPriorityQueue, 5.1.0"
-#r "nuget: FsMath, 0.0.1"
+#r "nuget: FsMath, 0.0.2"
 #I "../src/FSharp.Stats/bin/Release/.net8.0/"
 #r "FSharp.Stats.dll"
 #r "nuget: Plotly.NET, 4.0.0"

diff --git a/src/FSharp.Stats/Distributions/Continuous/StudentT.fs b/src/FSharp.Stats/Distributions/Continuous/StudentT.fs
@@ -18,9 +18,9 @@ type StudentT =
 
     // Student's T-distribution helper functions.
     static member CheckParam mu tau dof = 
-        if System.Double.IsNaN(mu) || tau < 0.0 || System.Double.IsNaN(dof)  || dof < 0. then 
+        if isNan(mu) || tau < 0.0 || isNan(dof)  || dof < 0. then 
             failwith "Student's T-distribution should be parametrized by mu, tau and dof > 0.0."
-
+            
     /// <summary>Computes the mode.</summary>
     /// <remarks></remarks>
     /// <param name="mu"></param>

diff --git a/src/FSharp.Stats/Distributions/Discrete/Hypergeometric.fs b/src/FSharp.Stats/Distributions/Discrete/Hypergeometric.fs
@@ -250,9 +250,8 @@ type Hypergeometric =
     static member ToString N K n  =
         sprintf "Hypergeometric(N = %i, K = %i, n = %i)" N K n 
 
-    /// <summary> Initializes a hypergeometric distribution.
-    /// 
-    /// <summary>
+    /// <summary> 
+    /// Initializes a hypergeometric distribution.
     /// The hypergeometric distribution is a discrete probability distribution
     /// that describes the probability of `k` successes (random draws for which the object
     /// drawn has a specified feature) in `n` draws, without replacement, from a finite

diff --git a/src/FSharp.Stats/Distributions/Discrete/NegativeBinomial.fs b/src/FSharp.Stats/Distributions/Discrete/NegativeBinomial.fs
@@ -374,11 +374,10 @@ type NegativeBinomial_failures =
     static member ToString r p = 
         sprintf "NegativeBinomial_failures(r = %i, p = %f)" r p
 
-    /// <summary> Initializes a negative binomial distribution.
-    /// <summary>The negative binomial distribution is a discrete probability distribution<br />that models the number of failures needed k to get the rth success in repeated <br />independent Bernoulli trials with probability p.<br /></summary><br /><param name="r">The number of success states</param><br /><param name="p">The probability of each independent bernoulli trial</param><br /><param name="k">The number of failures before the rth success</param></summary>
+    /// <summary> Initializes a negative binomial distribution.<br /> The negative binomial distribution is a discrete probability distribution<br />that models the number of failures needed k to get the rth success in repeated <br />independent Bernoulli trials with probability p.<br /></summary>
     /// <remarks></remarks>
-    /// <param name="r"></param>
-    /// <param name="p"></param>
+    /// <param name="r">The number of success states</param>
+    /// <param name="p">The probability of each independent bernoulli trial</param>
     /// <returns></returns>
     /// <example>
     /// <code>

diff --git a/src/FSharp.Stats/FSharp.Stats.fsproj b/src/FSharp.Stats/FSharp.Stats.fsproj
@@ -172,7 +172,7 @@
     <PackageReference Include="FSharpAux.Core" Version="2.0.0" />
     <PackageReference Include="FSharpAux" Version="2.0.0" />
     <PackageReference Include="FSharpAux.IO" Version="2.0.0" />
-    <PackageReference Include="FsMath" Version="0.0.1" />
+    <PackageReference Include="FsMath" Version="0.0.2" />
     <PackageReference Include="OptimizedPriorityQueue" Version="5.1.0" />
     <PackageReference Include="Microsoft.SourceLink.GitHub" Version="1.0.0" PrivateAssets="All" />
   </ItemGroup>

diff --git a/src/FSharp.Stats/Fitting/CrossValidation.fs b/src/FSharp.Stats/Fitting/CrossValidation.fs
@@ -153,6 +153,7 @@ module CrossValidation =
     /// <summary>Computes a repeated shuffel-and-split cross validation<br />p: percentage of training set size from original size,<br />iterations: number of random subset creation,<br />xData: rowwise x-coordinate matrix,<br />yData: yData vector<br />fit: x and y data lead to function that maps a xData row vector to a y-coordinate,<br />error: defines the error of the fitted y-coordinate and the actual y-coordinate,<br />getStDev: function that calculates the standard deviation from a seq&lt;^T&gt;. (Seq.stDev)</summary>
     /// <remarks></remarks>
     /// <param name="p"></param>
+    /// <param name="iterations"></param>
     /// <param name="xData"></param>
     /// <param name="yData"></param>
     /// <param name="fit"></param>

diff --git a/src/FSharp.Stats/Fitting/LinearRegression.fs b/src/FSharp.Stats/Fitting/LinearRegression.fs
@@ -156,6 +156,7 @@ module LinearRegression =
                 ///   Returns the regression function of a line through the origin
                 /// </summary>
                 /// <param name="coef">The functions slope</param>
+                /// <param name="x">x value of which the corresponding y value should be predicted</param>
                 /// <returns>Function that takes a x value and returns the predicted y value</returns>
                 /// <example> 
                 /// <code> 

diff --git a/src/FSharp.Stats/Fitting/LogisticRegression.fs b/src/FSharp.Stats/Fitting/LogisticRegression.fs
@@ -190,6 +190,7 @@ module LogisticRegression =
         /// <summary>Returns the regression function</summary>
         /// <remarks></remarks>
         /// <param name="coef"></param>
+        /// <param name="x"></param>
         /// <returns></returns>
         /// <example>
         /// <code>

diff --git a/src/FSharp.Stats/ML/Imputation.fs b/src/FSharp.Stats/ML/Imputation.fs
@@ -34,6 +34,8 @@ module Imputation =
     /// <summary>Imputation by random sampling from the input vector</summary>
     /// <remarks></remarks>
     /// <param name="rnd"></param>
+    /// <param name="fdata"></param>
+    /// <param name="index"></param>
     /// <returns></returns>
     /// <example>
     /// <code>
@@ -71,6 +73,9 @@ module Imputation =
     /// <summary>Imputation by k-nearest neighbour</summary>
     /// <remarks></remarks>
     /// <param name="k"></param>
+    /// <param name="data"></param>
+    /// <param name="arr"></param>
+    /// <param name="index"></param>
     /// <returns></returns>
     /// <example>
     /// <code>

diff --git a/src/FSharp.Stats/ML/Unsupervised/ClusterNumber.fs b/src/FSharp.Stats/ML/Unsupervised/ClusterNumber.fs
@@ -28,6 +28,8 @@ module ClusterNumber =
     /// <summary>Akaike Information Criterion (AIC)</summary>
     /// <remarks></remarks>
     /// <param name="bootstraps"></param>
+    /// <param name="iClustering"></param>
+    /// <param name="maxK"></param>
     /// <returns></returns>
     /// <example>
     /// <code>
@@ -299,6 +301,7 @@ https://www.datanovia.com/en/lessons/determining-the-optimal-number-of-clusters-
         /// <summary>Generate uniform points within the range of `data`.</summary>
         /// <remarks></remarks>
         /// <param name="rnd"></param>
+        /// <param name="data"></param>
         /// <returns></returns>
         /// <example>
         /// <code>

diff --git a/src/FSharp.Stats/ML/Unsupervised/KNN.fs b/src/FSharp.Stats/ML/Unsupervised/KNN.fs
@@ -207,6 +207,7 @@ module KNN =
         /// Predict (or classify) the given point.
         /// </summary>
         /// <param name="x">the point to be classified.</param>
+        /// <param name="overwriteK"></param>
         /// <example> 
         /// <code>
         /// // .. construct and fit the knnClassifier before ..
@@ -221,6 +222,7 @@ module KNN =
         /// Predict (or classify) the given collection of points.
         /// </summary>
         /// <param name="points">the array of points to be classified.</param>
+        /// <param name="overwriteK"></param>
         /// <example> 
         /// <code>
         /// // .. construct and fit the knnClassifier before ..