Add reference to Commons Math implementation of InternalErf, fix markdown issues, rename prob package in examples to stat

examples/src/main/kotlin/kscience/kmath/stat/DistributionBenchmark.kt

@@ -1,11 +1,8 @@
-package kscience.kmath.commons.prob
+package kscience.kmath.stat
 
 import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.async
 import kotlinx.coroutines.runBlocking
-import kscience.kmath.stat.RandomGenerator
-import kscience.kmath.stat.blocking
-import kscience.kmath.stat.fromSource
 import kscience.kmath.stat.samplers.GaussianSampler
 import org.apache.commons.rng.simple.RandomSource
 import java.time.Duration

examples/src/main/kotlin/kscience/kmath/stat/DistributionDemo.kt

@@ -1,4 +1,4 @@
-package kscience.kmath.commons.prob
+package kscience.kmath.stat
 
 import kotlinx.coroutines.runBlocking
 import kscience.kmath.chains.Chain

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/distributions/NormalDistribution.kt

@@ -9,6 +9,9 @@ import kscience.kmath.stat.samplers.NormalizedGaussianSampler
 import kscience.kmath.stat.samplers.ZigguratNormalizedGaussianSampler
 import kotlin.math.*
 
+/**
+ * Implements [UnivariateDistribution] for the normal (gaussian) distribution.
+ */
 public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
     public constructor(
         mean: Double,

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/internal/InternalErf.kt

@@ -2,6 +2,10 @@ package kscience.kmath.stat.internal
 
 import kotlin.math.abs
 
+/**
+ * Based on Commons Math implementation.
+ * See [https://commons.apache.org/proper/commons-math/javadocs/api-3.3/org/apache/commons/math3/special/Erf.html].
+ */
 internal object InternalErf {
     fun erfc(x: Double): Double {
         if (abs(x) > 40) return if (x > 0) 0.0 else 2.0

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AhrensDieterExponentialSampler.kt

@@ -12,7 +12,7 @@ import kotlin.math.pow
  * Sampling from an [exponential distribution](http://mathworld.wolfram.com/ExponentialDistribution.html).
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html].
  */
 public class AhrensDieterExponentialSampler private constructor(public val mean: Double) : Sampler<Double> {
     public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt

@@ -15,7 +15,8 @@ import kotlin.math.*
  *    Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html
+ *
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html].
  */
 public class AhrensDieterMarsagliaTsangGammaSampler private constructor(
     alpha: Double,

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AliasMethodDiscreteSampler.kt

@@ -34,7 +34,7 @@ import kotlin.math.min
  * that exploit the power of 2.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html].
  */
 public open class AliasMethodDiscreteSampler private constructor(
     // Deliberate direct storage of input arrays

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/BoxMullerNormalizedGaussianSampler.kt

@@ -11,7 +11,7 @@ import kotlin.math.*
  * distribution.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html].
  */
 public class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
     private var nextGaussian: Double = Double.NaN

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/GaussianSampler.kt

@@ -9,7 +9,7 @@ import kscience.kmath.stat.Sampler
  * Sampling from a Gaussian distribution with given mean and standard deviation.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html].
  *
  * @property mean the mean of the distribution.
  * @property standardDeviation the variance of the distribution.

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/KempSmallMeanPoissonSampler.kt

@@ -16,7 +16,7 @@ import kotlin.math.exp
  * Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html].
  */
 public class KempSmallMeanPoissonSampler private constructor(
     private val p0: Double,

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/LargeMeanPoissonSampler.kt

@@ -18,7 +18,7 @@ import kotlin.math.*
  * This sampler is suitable for mean >= 40.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html].
  */
 public class LargeMeanPoissonSampler private constructor(public val mean: Double) : Sampler<Int> {
     private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/MarsagliaNormalizedGaussianSampler.kt

@@ -13,7 +13,7 @@ import kotlin.math.sqrt
  * [BoxMullerNormalizedGaussianSampler].
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html]
  */
 public class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
     private var nextGaussian = Double.NaN

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/PoissonSampler.kt

@@ -14,7 +14,7 @@ import kscience.kmath.stat.Sampler
  *   Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html].
  */
 public class PoissonSampler private constructor(mean: Double) : Sampler<Int> {
     private val poissonSamplerDelegate: Sampler<Int> = of(mean)

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/SmallMeanPoissonSampler.kt

@@ -17,7 +17,7 @@ import kotlin.math.exp
  *
  * Based on Commons RNG implementation.
  *
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html].
  */
 public class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
     private val p0: Double = exp(-mean)

kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/ZigguratNormalizedGaussianSampler.kt

@@ -12,7 +12,7 @@ import kotlin.math.*
  * implementation has been adapted from the C code provided therein.
  *
  * Based on Commons RNG implementation.
- * See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html].
  */
 public class ZigguratNormalizedGaussianSampler private constructor() :
     NormalizedGaussianSampler, Sampler<Double> {