- Fix xml doc errors

- Fix typo (VCrossvalidation > shuffelAndSplit -> shuffleAndSplit)

Author: kMutagene

src/FSharp.Stats/Array.fs

@@ -645,7 +645,7 @@ module ArrayExtension =
         /// </summary>
         /// <param name="start">start value (is included)</param>
         /// <param name="stop">end value (by default is included )</param>
-        /// <param name="um">sets the number of elements in the array. If not set, stepsize = 1.</param>
+        /// <param name="num">sets the number of elements in the array. If not set, stepsize = 1.</param>
         /// <param name="IncludeEndpoint">If false, the array does not contain the stop value</param>
         static member linspace(start:float,stop:float,num:int,?IncludeEndpoint:bool) : float [] = 
 

src/FSharp.Stats/Correlation.fs

@@ -298,19 +298,19 @@ module Correlation =
             /// <summary>
             /// Tau A - Make no adjustments for ties
             /// </summary>
-            /// <param name="x">The first array of observations.</param>
-            /// <param name="y">The second array of observations.</param>
+            /// <param name="_x">The first array of observations.</param>
+            /// <param name="_y">The second array of observations.</param>
             /// <param name="pq">Number of concordant minues the number of discordant pairs.</param>
             /// <param name="n0">n(n-1)/2 or (n choose 2), where n is the number of observations.</param>
-            /// <param name="n1">sum_i(t_i(t_i-1)/2) where t_is is t_i he number of pairs of observations with the same x value.</param>
-            /// <param name="n2">sum_i(u_i(u_i-1)/2) where u_is is u_i he number of pairs of observations with the same y value.</param>
+            /// <param name="_n1">sum_i(t_i(t_i-1)/2) where t_is is t_i he number of pairs of observations with the same x value.</param>
+            /// <param name="_n2">sum_i(u_i(u_i-1)/2) where u_is is u_i he number of pairs of observations with the same y value.</param>
             /// <returns>The Kendall tau A statistic.</returns>
             let tauA _x _y pq n0 _n1 _n2  = pq / float n0
             /// <summary>
             /// Tau B - Adjust for ties. tau_b = pq / sqrt((n0 - n1)(n0 - n2))
             /// </summary>
-            /// <param name="x">The first array of observations.</param>
-            /// <param name="y">The second array of observations.</param>
+            /// <param name="_x">The first array of observations.</param>
+            /// <param name="_y">The second array of observations.</param>
             /// <param name="pq">Number of concordant minues the number of discordant pairs.</param>
             /// <param name="n0">n(n-1)/2 or (n choose 2), where n is the number of observations.</param>
             /// <param name="n1">sum_i(t_i(t_i-1)/2) where t_is is t_i he number of pairs of observations with the same x value.</param>
@@ -325,9 +325,9 @@ module Correlation =
             /// <param name="x">The first array of observations.</param>
             /// <param name="y">The second array of observations.</param>
             /// <param name="pq">Number of concordant minues the number of discordant pairs.</param>
-            /// <param name="n0">n(n-1)/2 or (n choose 2), where n is the number of observations.</param>
-            /// <param name="n1">sum_i(t_i(t_i-1)/2) where t_is is t_i he number of pairs of observations with the same x value.</param>
-            /// <param name="n2">sum_i(u_i(u_i-1)/2) where u_is is u_i he number of pairs of observations with the same y value.</param>
+            /// <param name="_n0">n(n-1)/2 or (n choose 2), where n is the number of observations.</param>
+            /// <param name="_n1">sum_i(t_i(t_i-1)/2) where t_is is t_i he number of pairs of observations with the same x value.</param>
+            /// <param name="_n2">sum_i(u_i(u_i-1)/2) where u_is is u_i he number of pairs of observations with the same y value.</param>
             /// <returns>The Kendall tau C statistic.</returns>
             let tauC (x : _[]) y pq _n0 _n1 _n2 = 
                 let n = x.Length

src/FSharp.Stats/DistanceMetrics.fs

@@ -124,8 +124,8 @@ module DistanceMetrics =
 
         /// <summary>The [Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) between two vectors of order `p`.</summary>
         /// <remarks>The two vectors need not have equal lengths: when one vectors is exhausted any remaining elements in the other vectors are ignored.</remarks>
-        /// <param name="s1">first vector</param>
-        /// <param name="s2">second vector</param>
+        /// <param name="v1">first vector</param>
+        /// <param name="v2">second vector</param>
         /// <param name="p">float constrained to `p > 0`</param>
         /// <returns>Minkowski distance between elements of given vectors. Returns NaN if vectors contain NaN.</returns>
         /// <example> 
@@ -163,8 +163,8 @@ module DistanceMetrics =
         /// <summary>The [Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) between two vectors (ignores NaN) of order `p`.</summary>
         /// <remarks>Non-regular differences between the sequences are ignored.
         /// The two vectors need not have equal lengths: when one vectors is exhausted any remaining elements in the other vectors are ignored.</remarks>
-        /// <param name="s1">first vector</param>
-        /// <param name="s2">second vector</param>
+        /// <param name="v1">first vector</param>
+        /// <param name="v2">second vector</param>
         /// <param name="p">float constrained to `p > 0`</param>
         /// <returns>Minkowski distance between elements of given vectors.</returns>
         /// <example> 
@@ -319,8 +319,8 @@ module DistanceMetrics =
 
         /// <summary>The [Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) between two arrays of order `p`.</summary>
         /// <remarks>The two arrays need not have equal lengths: when one array is exhausted any remaining elements in the other array are ignored.</remarks>
-        /// <param name="s1">first array</param>
-        /// <param name="s2">second array</param>
+        /// <param name="a1">first array</param>
+        /// <param name="a2">second array</param>
         /// <param name="p">float constrained to `p > 0`</param>
         /// <returns>Minkowski distance between elements of given arrays. Returns NaN if arrays contain NaN.</returns>
         /// <example> 
@@ -358,8 +358,8 @@ module DistanceMetrics =
         /// <summary>The [Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) between two arrays (ignores NaN) of order `p`.</summary>
         /// <remarks>Non-regular differences between the sequences are ignored.
         /// The two arrays need not have equal lengths: when one array is exhausted any remaining elements in the other array are ignored.</remarks>
-        /// <param name="s1">first array</param>
-        /// <param name="s2">second array</param>
+        /// <param name="a1">first array</param>
+        /// <param name="a2">second array</param>
         /// <param name="p">float constrained to `p > 0`</param>
         /// <returns>Minkowski distance between elements of given arrays.</returns>
         /// <example> 

src/FSharp.Stats/Distributions/Continuous/ChiSquared.fs

@@ -67,7 +67,7 @@ type ChiSquared =
         ChiSquared.CheckParam dof
         sqrt (dof * 2.)
 
-    /// <param name="k">Degrees of freedom (must be positive).</param>
+    /// <param name="dof">Degrees of freedom (must be positive).</param>
     /// <returns>A sample from χ²(dof), interpreted as Gamma(dof/2, 2).</returns>
     static member SampleUnchecked (dof: float) : float =
         let alpha = dof / 2.0

src/FSharp.Stats/Distributions/Discrete/Binomial.fs

@@ -82,7 +82,6 @@ type Binomial =
 
     /// <summary>Produces a random sample using the current random number generator (from GetSampleGenerator()).<br />No parameter checking!</summary>
     /// <remarks></remarks>
-    /// <param name="SampleUnchecked"></param>
     /// <param name="p"></param>
     /// <param name="n"></param>
     /// <returns></returns>

src/FSharp.Stats/Distributions/Discrete/Hypergeometric.fs

@@ -100,7 +100,6 @@ type Hypergeometric =
 
     /// <summary>Produces a random sample using the current random number generator (from GetSampleGenerator()).<br />No parameter checking!</summary>
     /// <remarks></remarks>
-    /// <param name="SampleUnchecked"></param>
     /// <param name="N"></param>
     /// <param name="K"></param>
     /// <param name="n"></param>
@@ -253,11 +252,17 @@ type Hypergeometric =
 
     /// <summary> Initializes a hypergeometric distribution.
     /// 
-    /// <summary>The hypergeometric distribution is a discrete probability distribution<br />that describes the probability of `k` successes (random draws for which the object<br />drawn has a specified feature) in `n` draws, without replacement, from a finite<br />population of size `N` that contains exactly `K` objects with that feature,<br />wherein each draw is either a success (`1.0`) or a failure (`0.0`).</summary><br /><param name="N">The population size</param><br /><param name="K">The number of success states in the population</param><br /><param name="n">The number of draws</param></summary>
+    /// <summary>
+    /// 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
+    /// population of size `N` that contains exactly `K` objects with that feature,
+    /// wherein each draw is either a success (`1.0`) or a failure (`0.0`).
+    /// </summary>
+    /// <param name="N">The population size</param>
+    /// <param name="K">The number of success states in the population</param>
+    /// <param name="n">The number of draws</param>
     /// <remarks></remarks>
-    /// <param name="N"></param>
-    /// <param name="K"></param>
-    /// <param name="n"></param>
     /// <returns></returns>
     /// <example>
     /// <code>

src/FSharp.Stats/Distributions/Discrete/NegativeBinomial.fs

@@ -1,4 +1,4 @@
-namespace FSharp.Stats.Distributions.Discrete
+Fkspace FSharp.Stats.Distributions.Discrete
 
 open System
 open FSharp.Stats
@@ -154,10 +154,13 @@ type NegativeBinomial_trials =
         sprintf "NegativeBinomial_trials(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 trials needed x 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="x">The number of trials until the rth success</param></summary>
+    /// The negative binomial distribution is a discrete probability distribution
+    /// that models the number of trials needed x to get the rth success in repeated 
+    /// independent Bernoulli trials with probability p.
+    /// </summary>
+    /// <param name="r">The number of success states</param>
+    /// <param name="p">The probability of each independent bernoulli trial</param>
     /// <remarks></remarks>
-    /// <param name="r"></param>
-    /// <param name="p"></param>
     /// <returns></returns>
     /// <example>
     /// <code>

src/FSharp.Stats/Distributions/Discrete/Poisson.fs

@@ -86,7 +86,6 @@ type Poisson =
 
     /// <summary>Produces a random sample using the current random number generator (from GetSampleGenerator()).<br />No parameter checking!</summary>
     /// <remarks></remarks>
-    /// <param name="SampleUnchecked"></param>
     /// <param name="lambda"></param>
     /// <returns></returns>
     /// <example>

src/FSharp.Stats/Distributions/Distance.fs

@@ -201,13 +201,13 @@ module Distance =
         /// <param name="xs"></param>
         /// <param name="ys"></param>
         /// <param name="xWeights"></param>
-        /// <param name="yWeights "></param>
+        /// <param name="yWeights"></param>
         /// <returns></returns>
         /// <example>
         /// <code>
         /// </code>
         /// </example>
-        let cdfDistanceWeighted p (xs : float []) (ys : float [])  xWeights yWeights =
+        let cdfDistanceWeighted p (xs : float []) (ys : float []) xWeights yWeights =
 
             if xs.Length = 0 then failwith "Given distribtuion xs is empty"
             if ys.Length = 0 then failwith "Given distribtuion ys is empty"

src/FSharp.Stats/Distributions/Empirical.fs

@@ -327,8 +327,8 @@ module Empirical =
     /// <summary>Merges two maps into a single map. If a key exists in both maps, the value is determined by f with the first value being from mapA and the second originating from mapB.</summary>
     /// <param name="equalBandwidthOrNominal">Is the binwidth equal for both distributions? For nominal data set to true.</param>
     /// <param name="f">Function to transform values if key is present in both histograms. `histA-value &#8594; histB-value &#8594; newValue`</param>
-    /// <param name="mapA">Empirical distribution A</param>
-    /// <param name="mapB">Empirical distribution B</param>
+    /// <param name="histA">Empirical distribution A</param>
+    /// <param name="histB">Empirical distribution B</param>
     /// <remarks>When applied to continuous data the bandwidths must be equal!</remarks> 
     /// <remarks>This function is not commutative! (mergeBy f a b) is not equal to (mergeBy f b a)</remarks> 
     /// <returns>New frequency map that results from merged maps mapA and mapB. Values from keys that are present in both maps are handled by f</returns>