kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/zSNormalCdf.kt

@@ -3,19 +3,19 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
-package space.kscience.kmath.series
+package space.kscience.kmath.distributions
 
 import space.kscience.kmath.operations.DoubleField.pow
 import kotlin.math.PI
 import kotlin.math.absoluteValue
 import kotlin.math.exp
 
-public fun zSNormalCDF(x: Double): Double {
 
 /**
  * Zelen & Severo approximation for the standard normal CDF.
  * The error is bounded by 7.5 * 10e-8.
  * */
+public fun zSNormalCDF(x: Double): Double {
 
     val t = 1 / (1 + 0.2316419 * x.absoluteValue)
     val summ = 0.319381530*t - 0.356563782*t.pow(2) + 1.781477937*t.pow(3) - 1.821255978*t.pow(4) + 1.330274429*t.pow(5)

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/SeriesAlgebra.kt

@@ -216,7 +216,7 @@ public open class SeriesAlgebra<T, out A : Ring<T>, out BA : BufferAlgebra<T, A>
 
     override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = left.zip(right) { l, r -> l * r }
 
-    public fun Buffer<T>.diff(shift: Int=1): Buffer<T> = this.zipWithShift(shift) {l, r -> r - l}
+    public fun Buffer<T>.difference(shift: Int=1): Buffer<T> = this.zipWithShift(shift) {l, r -> r - l}
 
     public companion object
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/VarianceRatioTest.kt

@@ -5,6 +5,7 @@
 
 package space.kscience.kmath.series
 
+import space.kscience.kmath.distributions.zSNormalCDF
 import space.kscience.kmath.operations.DoubleField.pow
 import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.operations.bufferAlgebra
@@ -12,14 +13,12 @@ import space.kscience.kmath.operations.fold
 import kotlin.math.absoluteValue
 
 
-// TODO: add p-value with formula: 2*(1 - cdf(|zScore|))
-public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zScore: Double=0.0, val pValue: Double=0.5)
 /**
  * Container class for Variance Ratio Test result:
  * ratio itself, corresponding Z-score, also it's p-value
  * **/
+public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zScore: Double=0.0, val pValue: Double=0.5)
 
-public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic: Boolean=true): VarianceRatioTestResult {
 
 /**
  * Calculates the Z-statistic and the p-value for the Lo and MacKinlay's Variance Ratio test (1987)
@@ -27,14 +26,15 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
  * with two-sided p-value test
  * 	https://ssrn.com/abstract=346975
  * **/
+public fun SeriesAlgebra<Double, *, *, *>.varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic: Boolean=true): VarianceRatioTestResult {
 
     require(shift > 1) {"Shift must be greater than one"}
     require(shift < series.size) {"Shift must be smaller than sample size"}
     val sum = { x: Double, y: Double -> x + y }
 
-    with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
+
     val mean = series.fold(0.0, sum) / series.size
-        val demeanedSquares = series.map { power(it - mean, 2) }
+    val demeanedSquares = series.map { (it - mean).pow(2) }
     val variance = demeanedSquares.fold(0.0, sum)
     if (variance == 0.0) return VarianceRatioTestResult()
 
@@ -44,7 +44,7 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
         seriesAgg = seriesAgg.zip(series.moveTo(i)) { v1, v2 -> v1 + v2 }
     }
 
-        val demeanedSquaresAgg = seriesAgg.map { power(it - shift * mean, 2) }
+    val demeanedSquaresAgg = seriesAgg.map { (it - shift * mean).pow(2) }
     val varianceAgg = demeanedSquaresAgg.fold(0.0, sum)
 
     val varianceRatio =
@@ -68,7 +68,6 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
     val pValue = 2*(1 - zSNormalCDF(zScore.absoluteValue))
     return VarianceRatioTestResult(varianceRatio, zScore, pValue)
 }
-}