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commit 84677cd0db3c1fbd02750c74c8c12a0c2dc2cd0f
Author: aherbert <aherb...@apache.org>
AuthorDate: Wed Oct 13 14:01:08 2021 +0100

    Updated to use factory constructors for Statistics distributions
---
 .../commons/math4/legacy/distribution/EmpiricalDistribution.java  | 4 ++--
 .../math4/legacy/distribution/MultivariateNormalDistribution.java | 2 +-
 .../legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java     | 2 +-
 .../math4/legacy/stat/correlation/PearsonsCorrelation.java        | 2 +-
 .../apache/commons/math4/legacy/stat/inference/BinomialTest.java  | 2 +-
 .../apache/commons/math4/legacy/stat/inference/ChiSquareTest.java | 6 +++---
 .../org/apache/commons/math4/legacy/stat/inference/GTest.java     | 6 +++---
 .../commons/math4/legacy/stat/inference/MannWhitneyUTest.java     | 2 +-
 .../apache/commons/math4/legacy/stat/inference/OneWayAnova.java   | 4 ++--
 .../org/apache/commons/math4/legacy/stat/inference/TTest.java     | 6 +++---
 .../math4/legacy/stat/inference/WilcoxonSignedRankTest.java       | 2 +-
 .../commons/math4/legacy/stat/interval/AgrestiCoullInterval.java  | 2 +-
 .../math4/legacy/stat/interval/ClopperPearsonInterval.java        | 8 ++++----
 .../math4/legacy/stat/interval/NormalApproximationInterval.java   | 2 +-
 .../commons/math4/legacy/stat/interval/WilsonScoreInterval.java   | 2 +-
 .../commons/math4/legacy/stat/regression/SimpleRegression.java    | 4 ++--
 16 files changed, 28 insertions(+), 28 deletions(-)

diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
index cca2c19..8058ea5 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
@@ -551,8 +551,8 @@ public final class EmpiricalDistribution extends 
AbstractRealDistribution
                 stats.getVariance() == 0) {
                 return new ConstantContinuousDistribution(stats.getMean());
             } else {
-                return new NormalDistribution(stats.getMean(),
-                                              stats.getStandardDeviation());
+                return NormalDistribution.of(stats.getMean(),
+                                             stats.getStandardDeviation());
             }
         };
     }
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java
index 6a72dbb..b216816 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java
@@ -181,7 +181,7 @@ public class MultivariateNormalDistribution
     public MultivariateRealDistribution.Sampler createSampler(final 
UniformRandomProvider rng) {
         return new MultivariateRealDistribution.Sampler() {
             /** Normal distribution. */
-            private final ContinuousDistribution.Sampler gauss = new 
NormalDistribution(0, 1).createSampler(rng);
+            private final ContinuousDistribution.Sampler gauss = 
NormalDistribution.of(0, 1).createSampler(rng);
 
             /** {@inheritDoc} */
             @Override
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
index 83b036e..eb92f7b 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java
@@ -246,7 +246,7 @@ public class CMAESOptimizer
         this.isActiveCMA = isActiveCMA;
         this.diagonalOnly = diagonalOnly;
         this.checkFeasableCount = Math.max(0, checkFeasableCount);
-        this.random = new NormalDistribution(0, 1).createSampler(rng);
+        this.random = NormalDistribution.of(0, 1).createSampler(rng);
         this.generateStatistics = generateStatistics;
     }
 
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java
index 34c2f6d..eda73a6 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java
@@ -192,7 +192,7 @@ public class PearsonsCorrelation {
      * @throws NullPointerException if this instance was created with no data.
      */
     public RealMatrix getCorrelationPValues() {
-        TDistribution tDistribution = new TDistribution(nObs - 2);
+        TDistribution tDistribution = TDistribution.of(nObs - 2);
         int nVars = correlationMatrix.getColumnDimension();
         double[][] out = new double[nVars][nVars];
         for (int i = 0; i < nVars; i++) {
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
index 011b7aa..80650c9 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
@@ -119,7 +119,7 @@ public class BinomialTest {
             throw new NullArgumentException();
         }
 
-        final BinomialDistribution distribution = new 
BinomialDistribution(numberOfTrials, probability);
+        final BinomialDistribution distribution = 
BinomialDistribution.of(numberOfTrials, probability);
         switch (alternativeHypothesis) {
         case GREATER_THAN:
             return 1 - distribution.cumulativeProbability(numberOfSuccesses - 
1);
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
index 92f43a0..e695d3f 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
@@ -157,7 +157,7 @@ public class ChiSquareTest {
 
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
         final ChiSquaredDistribution distribution =
-            new ChiSquaredDistribution(expected.length - 1.0);
+            ChiSquaredDistribution.of(expected.length - 1.0);
         return 1.0 - distribution.cumulativeProbability(chiSquare(expected, 
observed));
     }
 
@@ -332,7 +332,7 @@ public class ChiSquareTest {
         checkArray(counts);
         double df = ((double) counts.length -1) * ((double) counts[0].length - 
1);
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final ChiSquaredDistribution distribution = new 
ChiSquaredDistribution(df);
+        final ChiSquaredDistribution distribution = 
ChiSquaredDistribution.of(df);
         return 1 - distribution.cumulativeProbability(chiSquare(counts));
 
     }
@@ -536,7 +536,7 @@ public class ChiSquareTest {
 
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
         final ChiSquaredDistribution distribution =
-                new ChiSquaredDistribution((double) observed1.length - 1);
+                ChiSquaredDistribution.of((double) observed1.length - 1);
         return 1 - distribution.cumulativeProbability(
                 chiSquareDataSetsComparison(observed1, observed2));
 
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
index baaefb3..6dddb4a 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
@@ -154,7 +154,7 @@ public class GTest {
 
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
         final ChiSquaredDistribution distribution =
-                new ChiSquaredDistribution(expected.length - 1.0);
+                ChiSquaredDistribution.of(expected.length - 1.0);
         return 1.0 - distribution.cumulativeProbability(g(expected, observed));
     }
 
@@ -185,7 +185,7 @@ public class GTest {
 
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
         final ChiSquaredDistribution distribution =
-                new ChiSquaredDistribution(expected.length - 2.0);
+                ChiSquaredDistribution.of(expected.length - 2.0);
         return 1.0 - distribution.cumulativeProbability(g(expected, observed));
     }
 
@@ -475,7 +475,7 @@ public class GTest {
 
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
         final ChiSquaredDistribution distribution =
-                new ChiSquaredDistribution((double) observed1.length - 1);
+                ChiSquaredDistribution.of((double) observed1.length - 1);
         return 1 - distribution.cumulativeProbability(
                 gDataSetsComparison(observed1, observed2));
     }
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java
index be14563..2f1cdd3 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java
@@ -181,7 +181,7 @@ public class MannWhitneyUTest {
 
         // No try-catch or advertised exception because args are valid
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final NormalDistribution standardNormal = new NormalDistribution(0, 1);
+        final NormalDistribution standardNormal = NormalDistribution.of(0, 1);
 
         return 2 * standardNormal.cumulativeProbability(z);
     }
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
index e76cb20..d477427 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
@@ -126,7 +126,7 @@ public class OneWayAnova {
         final AnovaStats a = anovaStats(categoryData);
         // No try-catch or advertised exception because args are valid
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
+        final FDistribution fdist = FDistribution.of(a.dfbg, a.dfwg);
         return 1.0 - fdist.cumulativeProbability(a.f);
 
     }
@@ -168,7 +168,7 @@ public class OneWayAnova {
 
         final AnovaStats a = anovaStats(categoryData, allowOneElementData);
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
+        final FDistribution fdist = FDistribution.of(a.dfbg, a.dfwg);
         return 1.0 - fdist.cumulativeProbability(a.f);
 
     }
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java
index 9854538..be4dc90 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java
@@ -1057,7 +1057,7 @@ public class TTest {
 
         final double t = AccurateMath.abs(t(m, mu, v, n));
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final TDistribution distribution = new TDistribution(n - 1);
+        final TDistribution distribution = TDistribution.of(n - 1);
         return 2.0 * distribution.cumulativeProbability(-t);
 
     }
@@ -1087,7 +1087,7 @@ public class TTest {
         final double t = AccurateMath.abs(t(m1, m2, v1, v2, n1, n2));
         final double degreesOfFreedom = df(v1, v2, n1, n2);
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final TDistribution distribution = new TDistribution(degreesOfFreedom);
+        final TDistribution distribution = TDistribution.of(degreesOfFreedom);
         return 2.0 * distribution.cumulativeProbability(-t);
 
     }
@@ -1117,7 +1117,7 @@ public class TTest {
         final double t = AccurateMath.abs(homoscedasticT(m1, m2, v1, v2, n1, 
n2));
         final double degreesOfFreedom = n1 + n2 - 2;
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final TDistribution distribution = new TDistribution(degreesOfFreedom);
+        final TDistribution distribution = TDistribution.of(degreesOfFreedom);
         return 2.0 * distribution.cumulativeProbability(-t);
 
     }
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java
index 8d7141d..0d1fdd4 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java
@@ -253,7 +253,7 @@ public class WilcoxonSignedRankTest {
 
         // No try-catch or advertised exception because args are valid
         // pass a null rng to avoid unneeded overhead as we will not sample 
from this distribution
-        final NormalDistribution standardNormal = new NormalDistribution(0, 1);
+        final NormalDistribution standardNormal = NormalDistribution.of(0, 1);
 
         return 2*standardNormal.cumulativeProbability(z);
     }
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
index 3ddb4cb..2e60415 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
@@ -34,7 +34,7 @@ public class AgrestiCoullInterval implements 
BinomialConfidenceInterval {
     public ConfidenceInterval createInterval(int numberOfTrials, int 
numberOfSuccesses, double confidenceLevel) {
         IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, 
confidenceLevel);
         final double alpha = (1.0 - confidenceLevel) / 2;
-        final NormalDistribution normalDistribution = new 
NormalDistribution(0, 1);
+        final NormalDistribution normalDistribution = NormalDistribution.of(0, 
1);
         final double z = normalDistribution.inverseCumulativeProbability(1 - 
alpha);
         final double zSquared = AccurateMath.pow(z, 2);
         final double modifiedNumberOfTrials = numberOfTrials + zSquared;
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
index 20f4913..a28522c 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
@@ -40,16 +40,16 @@ public class ClopperPearsonInterval implements 
BinomialConfidenceInterval {
         final double alpha = 0.5 * (1 - confidenceLevel);
 
         if (numberOfSuccesses > 0) {
-            final FDistribution distributionLowerBound = new FDistribution(2.0 
* (numberOfTrials - numberOfSuccesses + 1),
-                                                                           2.0 
* numberOfSuccesses);
+            final FDistribution distributionLowerBound = FDistribution.of(2.0 
* (numberOfTrials - numberOfSuccesses + 1),
+                                                                          2.0 
* numberOfSuccesses);
             final double fValueLowerBound = 
distributionLowerBound.inverseCumulativeProbability(1 - alpha);
             lowerBound = numberOfSuccesses /
                 (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) 
* fValueLowerBound);
         }
 
         if (numberOfSuccesses < numberOfTrials) {
-            final FDistribution distributionUpperBound = new FDistribution(2.0 
* (numberOfSuccesses + 1),
-                                                                           2.0 
* (numberOfTrials - numberOfSuccesses));
+            final FDistribution distributionUpperBound = FDistribution.of(2.0 
* (numberOfSuccesses + 1),
+                                                                          2.0 
* (numberOfTrials - numberOfSuccesses));
             final double fValueUpperBound = 
distributionUpperBound.inverseCumulativeProbability(1 - alpha);
             upperBound = (numberOfSuccesses + 1) * fValueUpperBound /
                 (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) 
* fValueUpperBound);
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
index 75533e4..c9e4832 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
@@ -36,7 +36,7 @@ public class NormalApproximationInterval implements 
BinomialConfidenceInterval {
         IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, 
confidenceLevel);
         final double mean = (double) numberOfSuccesses / (double) 
numberOfTrials;
         final double alpha = (1.0 - confidenceLevel) / 2;
-        final NormalDistribution normalDistribution = new 
NormalDistribution(0, 1);
+        final NormalDistribution normalDistribution = NormalDistribution.of(0, 
1);
         final double difference = 
normalDistribution.inverseCumulativeProbability(1 - alpha) *
                                   AccurateMath.sqrt(1.0 / numberOfTrials * 
mean * (1 - mean));
         return new ConfidenceInterval(mean - difference, mean + difference, 
confidenceLevel);
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
index 713f46e..0fa89ec 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
@@ -32,7 +32,7 @@ public class WilsonScoreInterval implements 
BinomialConfidenceInterval {
     public ConfidenceInterval createInterval(int numberOfTrials, int 
numberOfSuccesses, double confidenceLevel) {
         IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, 
confidenceLevel);
         final double alpha = (1 - confidenceLevel) / 2;
-        final NormalDistribution normalDistribution = new 
NormalDistribution(0, 1);
+        final NormalDistribution normalDistribution = NormalDistribution.of(0, 
1);
         final double z = normalDistribution.inverseCumulativeProbability(1 - 
alpha);
         final double zSquared = z * z;
         final double oneOverNumTrials = 1d / numberOfTrials;
diff --git 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
index c4b18dd..981f14d 100644
--- 
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
+++ 
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
@@ -698,7 +698,7 @@ public class SimpleRegression implements Serializable, 
UpdatingMultipleLinearReg
                                           alpha, 0, 1);
         }
         // No advertised NotStrictlyPositiveException here - will return NaN 
above
-        TDistribution distribution = new TDistribution(n - 2d);
+        TDistribution distribution = TDistribution.of(n - 2d);
         return getSlopeStdErr() *
             distribution.inverseCumulativeProbability(1d - alpha / 2d);
     }
@@ -730,7 +730,7 @@ public class SimpleRegression implements Serializable, 
UpdatingMultipleLinearReg
             return Double.NaN;
         }
         // No advertised NotStrictlyPositiveException here - will return NaN 
above
-        TDistribution distribution = new TDistribution(n - 2d);
+        TDistribution distribution = TDistribution.of(n - 2d);
         return 2d * (1.0 - distribution.cumulativeProbability(
                     AccurateMath.abs(getSlope()) / getSlopeStdErr()));
     }

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