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From raydeca...@apache.org
Subject [2/5] [math] Fix some javadoc issues.
Date Sun, 07 May 2017 16:36:53 GMT
http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java b/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java
index a0da22f..a06e3d1 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/InferenceTestUtils.java
@@ -65,6 +65,9 @@ public class InferenceTestUtils {
     // CHECKSTYLE: stop JavadocMethodCheck
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticT(double[], double[])
      */
     public static double homoscedasticT(final double[] sample1, final double[] sample2)
@@ -73,6 +76,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sampleStats1 StatisticalSummary describing data from the first sample
+     * @param sampleStats2 StatisticalSummary describing data from the second sample
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticT(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
      */
     public static double homoscedasticT(final StatisticalSummary sampleStats1,
@@ -82,6 +88,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @param alpha significance level of the test
+     * @return true if the null hypothesis can be rejected with
+     * confidence 1 - alpha
      * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(double[], double[], double)
      */
     public static boolean homoscedasticTTest(final double[] sample1, final double[] sample2,
@@ -92,6 +103,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @return p-value for t-test
      * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(double[], double[])
      */
     public static double homoscedasticTTest(final double[] sample1, final double[] sample2)
@@ -100,6 +114,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sampleStats1  StatisticalSummary describing data from the first sample
+     * @param sampleStats2  StatisticalSummary describing data from the second sample
+     * @return p-value for t-test
      * @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
      */
     public static double homoscedasticTTest(final StatisticalSummary sampleStats1,
@@ -109,6 +126,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#pairedT(double[], double[])
      */
     public static double pairedT(final double[] sample1, final double[] sample2)
@@ -118,6 +138,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @param alpha significance level of the test
+     * @return true if the null hypothesis can be rejected with
+     * confidence 1 - alpha
      * @see org.apache.commons.math4.stat.inference.TTest#pairedTTest(double[], double[], double)
      */
     public static boolean pairedTTest(final double[] sample1, final double[] sample2,
@@ -128,6 +153,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @return p-value for t-test
      * @see org.apache.commons.math4.stat.inference.TTest#pairedTTest(double[], double[])
      */
     public static double pairedTTest(final double[] sample1, final double[] sample2)
@@ -137,6 +165,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param mu comparison constant
+     * @param observed array of values
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#t(double, double[])
      */
     public static double t(final double mu, final double[] observed)
@@ -145,6 +176,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param mu comparison constant
+     * @param sampleStats DescriptiveStatistics holding sample summary statitstics
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#t(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
      */
     public static double t(final double mu, final StatisticalSummary sampleStats)
@@ -153,6 +187,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#t(double[], double[])
      */
     public static double t(final double[] sample1, final double[] sample2)
@@ -161,6 +198,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sampleStats1 StatisticalSummary describing data from the first sample
+     * @param sampleStats2 StatisticalSummary describing data from the second sample
+     * @return t statistic
      * @see org.apache.commons.math4.stat.inference.TTest#t(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
      */
     public static double t(final StatisticalSummary sampleStats1,
@@ -170,6 +210,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param mu constant value to compare sample mean against
+     * @param sample array of sample data values
+     * @param alpha significance level of the test
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, double[], double)
      */
     public static boolean tTest(final double mu, final double[] sample, final double alpha)
@@ -179,6 +223,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param mu constant value to compare sample mean against
+     * @param sample array of sample data values
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, double[])
      */
     public static double tTest(final double mu, final double[] sample)
@@ -188,6 +235,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param mu constant value to compare sample mean against
+     * @param sampleStats StatisticalSummary describing sample data values
+     * @param alpha significance level of the test
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary, double)
      */
     public static boolean tTest(final double mu, final StatisticalSummary sampleStats,
@@ -198,6 +249,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param mu constant value to compare sample mean against
+     * @param sampleStats StatisticalSummary describing sample data
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
      */
     public static double tTest(final double mu, final StatisticalSummary sampleStats)
@@ -207,6 +261,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @param alpha significance level of the test
+     * @return true if the null hypothesis can be rejected with
+     * confidence 1 - alpha
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(double[], double[], double)
      */
     public static boolean tTest(final double[] sample1, final double[] sample2,
@@ -217,6 +276,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sample1 array of sample data values
+     * @param sample2 array of sample data values
+     * @return p-value for t-test
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(double[], double[])
      */
     public static double tTest(final double[] sample1, final double[] sample2)
@@ -226,6 +288,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sampleStats1 StatisticalSummary describing sample data values
+     * @param sampleStats2 StatisticalSummary describing sample data values
+     * @param alpha significance level of the test
+     * @return true if the null hypothesis can be rejected with
+     * confidence 1 - alpha
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary, double)
      */
     public static boolean tTest(final StatisticalSummary sampleStats1,
@@ -237,6 +304,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param sampleStats1  StatisticalSummary describing data from the first sample
+     * @param sampleStats2  StatisticalSummary describing data from the second sample
+     * @return p-value for t-test
      * @see org.apache.commons.math4.stat.inference.TTest#tTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
      */
     public static double tTest(final StatisticalSummary sampleStats1,
@@ -247,7 +317,10 @@ public class InferenceTestUtils {
     }
 
     /**
-     * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(double[], long[])
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @return chiSquare test statistic
+* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(double[], long[])
      */
     public static double chiSquare(final double[] expected, final long[] observed)
         throws NotPositiveException, NotStrictlyPositiveException,
@@ -256,6 +329,8 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param counts array representation of 2-way table
+     * @return chiSquare test statistic
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(long[][])
      */
     public static double chiSquare(final long[][] counts)
@@ -265,6 +340,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @param alpha significance level of the test
+     * @return true iff null hypothesis can be rejected with confidence
+     * 1 - alpha
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(double[], long[], double)
      */
     public static boolean chiSquareTest(final double[] expected, final long[] observed,
@@ -275,6 +355,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(double[], long[])
      */
     public static double chiSquareTest(final double[] expected, final long[] observed)
@@ -284,6 +367,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param counts array representation of 2-way table
+     * @param alpha significance level of the test
+     * @return true iff null hypothesis can be rejected with confidence
+     * 1 - alpha
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(long[][], double)
      */
     public static boolean chiSquareTest(final long[][] counts, final double alpha)
@@ -293,6 +380,8 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param counts array representation of 2-way table
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(long[][])
      */
     public static double chiSquareTest(final long[][] counts)
@@ -302,6 +391,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed1 array of observed frequency counts of the first data set
+     * @param observed2 array of observed frequency counts of the second data set
+     * @return chiSquare test statistic
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareDataSetsComparison(long[], long[])
      *
      * @since 1.2
@@ -313,6 +405,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed1 array of observed frequency counts of the first data set
+     * @param observed2 array of observed frequency counts of the second data set
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTestDataSetsComparison(long[], long[])
      *
      * @since 1.2
@@ -325,6 +420,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed1 array of observed frequency counts of the first data set
+     * @param observed2 array of observed frequency counts of the second data set
+     * @param alpha significance level of the test
+     * @return true iff null hypothesis can be rejected with confidence
+     * 1 - alpha
      * @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTestDataSetsComparison(long[], long[], double)
      *
      * @since 1.2
@@ -338,6 +438,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param categoryData <code>Collection</code> of <code>double[]</code>
+     * arrays each containing data for one category
+     * @return Fvalue
      * @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaFValue(Collection)
      *
      * @since 1.2
@@ -348,6 +451,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param categoryData <code>Collection</code> of <code>double[]</code>
+     * arrays each containing data for one category
+     * @return Pvalue
      * @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaPValue(Collection)
      *
      * @since 1.2
@@ -359,6 +465,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param categoryData <code>Collection</code> of <code>double[]</code>
+     * arrays each containing data for one category
+     * @param alpha significance level of the test
+     * @return true if the null hypothesis can be rejected with
+     * confidence 1 - alpha
      * @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaTest(Collection,double)
      *
      * @since 1.2
@@ -371,6 +482,9 @@ public class InferenceTestUtils {
     }
 
      /**
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @return G-Test statistic
      * @see org.apache.commons.math4.stat.inference.GTest#g(double[], long[])
      * @since 3.1
      */
@@ -381,6 +495,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.GTest#gTest( double[],  long[] )
      * @since 3.1
      */
@@ -391,6 +508,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.GTest#gTestIntrinsic(double[], long[] )
      * @since 3.1
      */
@@ -401,6 +521,11 @@ public class InferenceTestUtils {
     }
 
      /**
+     * @param observed array of observed frequency counts
+     * @param expected array of expected frequency counts
+     * @param alpha significance level of the test
+     * @return true iff null hypothesis can be rejected with confidence 1 -
+     * alpha
      * @see org.apache.commons.math4.stat.inference.GTest#gTest( double[],long[],double)
      * @since 3.1
      */
@@ -412,6 +537,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed1 array of observed frequency counts of the first data set
+     * @param observed2 array of observed frequency counts of the second data
+     * set
+     * @return G-Test statistic
      * @see org.apache.commons.math4.stat.inference.GTest#gDataSetsComparison(long[], long[])
      * @since 3.1
      */
@@ -422,6 +551,14 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param k11 number of times the two events occurred together (AB)
+     * @param k12 number of times the second event occurred WITHOUT the
+     * first event (notA,B)
+     * @param k21 number of times the first event occurred WITHOUT the
+     * second event (A, notB)
+     * @param k22 number of times something else occurred (i.e. was neither
+     * of these events (notA, notB)
+     * @return root log-likelihood ratio
      * @see org.apache.commons.math4.stat.inference.GTest#rootLogLikelihoodRatio(long, long, long, long)
      * @since 3.1
      */
@@ -432,6 +569,10 @@ public class InferenceTestUtils {
 
 
     /**
+     * @param observed1 array of observed frequency counts of the first data set
+     * @param observed2 array of observed frequency counts of the second data
+     * set
+     * @return p-value
      * @see org.apache.commons.math4.stat.inference.GTest#gTestDataSetsComparison(long[], long[])
      * @since 3.1
      */
@@ -443,6 +584,12 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param observed1 array of observed frequency counts of the first data set
+     * @param observed2 array of observed frequency counts of the second data
+     * set
+     * @param alpha significance level of the test
+     * @return true iff null hypothesis can be rejected with confidence 1 -
+     * alpha
      * @see org.apache.commons.math4.stat.inference.GTest#gTestDataSetsComparison(long[],long[],double)
      * @since 3.1
      */
@@ -455,6 +602,9 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param dist reference distribution
+     * @param data sample being evaluated
+     * @return Kolmogorov-Smirnov statistic \(D_n\)
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovStatistic(RealDistribution, double[])
      * @since 3.3
      */
@@ -464,6 +614,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param dist reference distribution
+     * @param data sample being being evaluated
+     * @return the p-value associated with the null hypothesis that {@code data} is a sample from
+     *         {@code distribution}
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[])
      * @since 3.3
      */
@@ -473,6 +627,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param dist reference distribution
+     * @param data sample being being evaluated
+     * @param strict whether or not to force exact computation of the p-value
+     * @return the p-value associated with the null hypothesis that {@code data} is a sample from
+     *         {@code distribution}
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[], boolean)
      * @since 3.3
      */
@@ -482,6 +641,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param dist reference distribution
+     * @param data sample being being evaluated
+     * @param alpha significance level of the test
+     * @return true iff the null hypothesis that {@code data} is a sample from {@code distribution}
+     *         can be rejected with confidence 1 - {@code alpha}
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[], double)
      * @since 3.3
      */
@@ -491,6 +655,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param x first sample
+     * @param y second sample
+     * @return test statistic \(D_{n,m}\) used to evaluate the null hypothesis that {@code x} and
+     *         {@code y} represent samples from the same underlying distribution
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovStatistic(double[], double[])
      * @since 3.3
      */
@@ -500,6 +668,10 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param x first sample dataset
+     * @param y second sample dataset
+     * @return p-value associated with the null hypothesis that {@code x} and {@code y} represent
+     *         samples from the same distribution
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(double[], double[])
      * @since 3.3
      */
@@ -509,6 +681,12 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param x first sample dataset.
+     * @param y second sample dataset.
+     * @param strict whether or not the probability to compute is expressed as
+     * a strict inequality (ignored for large samples).
+     * @return p-value associated with the null hypothesis that {@code x} and
+     * {@code y} represent samples from the same distribution.
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(double[], double[], boolean)
      * @since 3.3
      */
@@ -518,6 +696,12 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param d D-statistic value
+     * @param n first sample size
+     * @param m second sample size
+     * @param strict whether or not the probability to compute is expressed as a strict inequality
+     * @return probability that a randomly selected m-n partition of m + n generates \(D_{n,m}\)
+     *         greater than (resp. greater than or equal to) {@code d}
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#exactP(double, int, int, boolean)
      * @since 3.3
      */
@@ -526,6 +710,11 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param d D-statistic value
+     * @param n first sample size
+     * @param m second sample size
+     * @return approximate probability that a randomly selected m-n partition of m + n generates
+     *         \(D_{n,m}\) greater than {@code d}
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#approximateP(double, int, int)
      * @since 3.3
      */
@@ -534,6 +723,13 @@ public class InferenceTestUtils {
     }
 
     /**
+     * @param d D-statistic value
+     * @param n first sample size
+     * @param m second sample size
+     * @param iterations number of random partitions to generate
+     * @param strict whether or not the probability to compute is expressed as a strict inequality
+     * @return proportion of randomly generated m-n partitions of m + n that result in \(D_{n,m}\)
+     *         greater than (resp. greater than or equal to) {@code d}
      * @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#monteCarloP(double, int, int, boolean, int)
      * @since 3.3
      */

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java b/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java
index c79e644..53a6162 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/KolmogorovSmirnovTest.java
@@ -73,7 +73,7 @@ import org.apache.commons.math4.util.MathUtils;
  * <li>When the product of the sample sizes exceeds {@value #LARGE_SAMPLE_PRODUCT}, the asymptotic
  * distribution of \(D_{n,m}\) is used. See {@link #approximateP(double, int, int)} for details on
  * the approximation.</li>
- * </ul></p><p>
+ * </ul><p>
  * If the product of the sample sizes is less than {@value #LARGE_SAMPLE_PRODUCT} and the sample
  * data contains ties, random jitter is added to the sample data to break ties before applying
  * the algorithm above. Alternatively, the {@link #bootstrap(double[], double[], int, boolean)}
@@ -82,7 +82,7 @@ import org.apache.commons.math4.util.MathUtils;
  * </p>
  * <p>
  * In the two-sample case, \(D_{n,m}\) has a discrete distribution. This makes the p-value
- * associated with the null hypothesis \(H_0 : D_{n,m} \ge d \) differ from \(H_0 : D_{n,m} > d \)
+ * associated with the null hypothesis \(H_0 : D_{n,m} \ge d \) differ from \(H_0 : D_{n,m} \ge d \)
  * by the mass of the observed value \(d\). To distinguish these, the two-sample tests use a boolean
  * {@code strict} parameter. This parameter is ignored for large samples.
  * </p>
@@ -95,7 +95,6 @@ import org.apache.commons.math4.util.MathUtils;
  * expressed using strict or non-strict inequality. See
  * {@link #kolmogorovSmirnovTest(double[], double[], boolean)}.</li>
  * </ul>
- * </p>
  * <p>
  * References:
  * <ul>
@@ -109,10 +108,9 @@ import org.apache.commons.math4.util.MathUtils;
  * <li>[4] Wilcox, Rand. 2012. Introduction to Robust Estimation and Hypothesis Testing,
  * Chapter 5, 3rd Ed. Academic Press.</li>
  * </ul>
- * <br/>
+ * <br>
  * Note that [1] contains an error in computing h, refer to <a
  * href="https://issues.apache.org/jira/browse/MATH-437">MATH-437</a> for details.
- * </p>
  *
  * @since 3.3
  */
@@ -234,7 +232,7 @@ public class KolmogorovSmirnovTest {
      * asymptotic distribution of \(D_{n,m}\) is used. See {@link #approximateP(double, int, int)}
      * for details on the approximation.</li>
      * </ul><p>
-     * If {@code x.length * y.length} < {@value #LARGE_SAMPLE_PRODUCT} and the combined set of values in
+     * If {@code x.length * y.length <} {@value #LARGE_SAMPLE_PRODUCT} and the combined set of values in
      * {@code x} and {@code y} contains ties, random jitter is added to {@code x} and {@code y} to
      * break ties before computing \(D_{n,m}\) and the p-value. The jitter is uniformly distributed
      * on (-minDelta / 2, minDelta / 2) where minDelta is the smallest pairwise difference between
@@ -457,17 +455,17 @@ public class KolmogorovSmirnovTest {
     }
 
     /**
-     * Calculates \(P(D_n < d)\) using the method described in [1] with quick decisions for extreme
+     * Calculates \(P(D_n &lt; d)\) using the method described in [1] with quick decisions for extreme
      * values given in [2] (see above). The result is not exact as with
      * {@link #cdfExact(double, int)} because calculations are based on
      * {@code double} rather than {@link org.apache.commons.math4.fraction.BigFraction}.
      *
      * @param d statistic
      * @param n sample size
-     * @return \(P(D_n < d)\)
+     * @return \(P(D_n &lt; d)\)
      * @throws MathArithmeticException if algorithm fails to convert {@code h} to a
      *         {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k
-     *         - h) / m\) for integer {@code k, m} and \(0 \le h < 1\)
+     *         - h) / m\) for integer {@code k, m} and \(0 \le h &lt; 1\)
      */
     public double cdf(double d, int n)
         throws MathArithmeticException {
@@ -483,10 +481,10 @@ public class KolmogorovSmirnovTest {
      *
      * @param d statistic
      * @param n sample size
-     * @return \(P(D_n < d)\)
+     * @return \(P(D_n &lt; d)\)
      * @throws MathArithmeticException if the algorithm fails to convert {@code h} to a
      *         {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k
-     *         - h) / m\) for integer {@code k, m} and \(0 \le h < 1\)
+     *         - h) / m\) for integer {@code k, m} and \(0 \le h &lt; 1\)
      */
     public double cdfExact(double d, int n)
         throws MathArithmeticException {
@@ -504,10 +502,10 @@ public class KolmogorovSmirnovTest {
      *        very slow execution time, or if {@code double} should be used convenient places to
      *        gain speed. Almost never choose {@code true} in real applications unless you are very
      *        sure; {@code true} is almost solely for verification purposes.
-     * @return \(P(D_n < d)\)
+     * @return \(P(D_n &lt; d)\)
      * @throws MathArithmeticException if algorithm fails to convert {@code h} to a
      *         {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k
-     *         - h) / m\) for integer {@code k, m} and \(0 \le h < 1\).
+     *         - h) / m\) for integer {@code k, m} and \(0 \le h &lt; 1\).
      */
     public double cdf(double d, int n, boolean exact)
         throws MathArithmeticException {
@@ -546,10 +544,10 @@ public class KolmogorovSmirnovTest {
      *
      * @param d statistic
      * @param n sample size
-     * @return the two-sided probability of \(P(D_n < d)\)
+     * @return the two-sided probability of \(P(D_n &lt; d)\)
      * @throws MathArithmeticException if algorithm fails to convert {@code h} to a
      *         {@link org.apache.commons.math4.fraction.BigFraction} in expressing {@code d} as \((k
-     *         - h) / m\) for integer {@code k, m} and \(0 \le h < 1\).
+     *         - h) / m\) for integer {@code k, m} and \(0 \le h &lt; 1\).
      */
     private double exactK(double d, int n)
         throws MathArithmeticException {
@@ -578,7 +576,7 @@ public class KolmogorovSmirnovTest {
      *
      * @param d statistic
      * @param n sample size
-     * @return \(P(D_n < d)\)
+     * @return \(P(D_n &lt; d)\)
      */
     private double roundedK(double d, int n) {
 
@@ -595,11 +593,11 @@ public class KolmogorovSmirnovTest {
     }
 
     /**
-     * Computes the Pelz-Good approximation for \(P(D_n < d)\) as described in [2] in the class javadoc.
+     * Computes the Pelz-Good approximation for \(P(D_n &lt; d)\) as described in [2] in the class javadoc.
      *
      * @param d value of d-statistic (x in [2])
      * @param n sample size
-     * @return \(P(D_n < d)\)
+     * @return \(P(D_n &lt; d)\)
      * @since 3.4
      */
     public double pelzGood(double d, int n) {
@@ -986,7 +984,7 @@ public class KolmogorovSmirnovTest {
     }
 
     /**
-     * Computes \(P(D_{n,m} > d)\) if {@code strict} is {@code true}; otherwise \(P(D_{n,m} \ge
+     * Computes \(P(D_{n,m} &gt; d)\) if {@code strict} is {@code true}; otherwise \(P(D_{n,m} \ge
      * d)\), where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic. See
      * {@link #kolmogorovSmirnovStatistic(double[], double[])} for the definition of \(D_{n,m}\).
      * <p>
@@ -1007,7 +1005,7 @@ public class KolmogorovSmirnovTest {
     }
 
     /**
-     * Uses the Kolmogorov-Smirnov distribution to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\)
+     * Uses the Kolmogorov-Smirnov distribution to approximate \(P(D_{n,m} &gt; d)\) where \(D_{n,m}\)
      * is the 2-sample Kolmogorov-Smirnov statistic. See
      * {@link #kolmogorovSmirnovStatistic(double[], double[])} for the definition of \(D_{n,m}\).
      * <p>
@@ -1052,7 +1050,7 @@ public class KolmogorovSmirnovTest {
     }
 
     /**
-     * Uses Monte Carlo simulation to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) is the
+     * Uses Monte Carlo simulation to approximate \(P(D_{n,m} &gt; d)\) where \(D_{n,m}\) is the
      * 2-sample Kolmogorov-Smirnov statistic. See
      * {@link #kolmogorovSmirnovStatistic(double[], double[])} for the definition of \(D_{n,m}\).
      * <p>

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java b/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java
index 4452816..f938c17 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/MannWhitneyUTest.java
@@ -114,7 +114,6 @@ public class MannWhitneyUTest {
      * <li>All observations in the two samples are independent.</li>
      * <li>The observations are at least ordinal (continuous are also ordinal).</li>
      * </ul>
-     * </p>
      *
      * @param x the first sample
      * @param y the second sample
@@ -203,8 +202,7 @@ public class MannWhitneyUTest {
      * <ul>
      * <li>All observations in the two samples are independent.</li>
      * <li>The observations are at least ordinal (continuous are also ordinal).</li>
-     * </ul>
-     * </p><p>
+     * </ul><p>
      * Ties give rise to biased variance at the moment. See e.g. <a
      * href="http://mlsc.lboro.ac.uk/resources/statistics/Mannwhitney.pdf"
      * >http://mlsc.lboro.ac.uk/resources/statistics/Mannwhitney.pdf</a>.</p>

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java b/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java
index 3c322c9..e54daf1 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/OneWayAnova.java
@@ -66,7 +66,7 @@ public class OneWayAnova {
      * <code>double[]</code> arrays.</li>
      * <li> There must be at least two <code>double[]</code> arrays in the
      * <code>categoryData</code> collection and each of these arrays must
-     * contain at least two values.</li></ul></p><p>
+     * contain at least two values.</li></ul><p>
      * This implementation computes the F statistic using the definitional
      * formula<pre>
      *   F = msbg/mswg</pre>
@@ -74,7 +74,7 @@ public class OneWayAnova {
      *  msbg = between group mean square
      *  mswg = within group mean square</pre>
      * are as defined <a href="http://faculty.vassar.edu/lowry/ch13pt1.html">
-     * here</a></p>
+     * here</a>
      *
      * @param categoryData <code>Collection</code> of <code>double[]</code>
      * arrays each containing data for one category
@@ -101,14 +101,14 @@ public class OneWayAnova {
      * <code>double[]</code> arrays.</li>
      * <li> There must be at least two <code>double[]</code> arrays in the
      * <code>categoryData</code> collection and each of these arrays must
-     * contain at least two values.</li></ul></p><p>
+     * contain at least two values.</li></ul><p>
      * This implementation uses the
      * {@link org.apache.commons.math4.distribution.FDistribution
      * commons-math F Distribution implementation} to estimate the exact
      * p-value, using the formula<pre>
      *   p = 1 - cumulativeProbability(F)</pre>
      * where <code>F</code> is the F value and <code>cumulativeProbability</code>
-     * is the commons-math implementation of the F distribution.</p>
+     * is the commons-math implementation of the F distribution.
      *
      * @param categoryData <code>Collection</code> of <code>double[]</code>
      * arrays each containing data for one category
@@ -140,14 +140,14 @@ public class OneWayAnova {
      * {@link SummaryStatistics}.</li>
      * <li> There must be at least two {@link SummaryStatistics} in the
      * <code>categoryData</code> collection and each of these statistics must
-     * contain at least two values.</li></ul></p><p>
+     * contain at least two values.</li></ul><p>
      * This implementation uses the
      * {@link org.apache.commons.math4.distribution.FDistribution
      * commons-math F Distribution implementation} to estimate the exact
      * p-value, using the formula<pre>
      *   p = 1 - cumulativeProbability(F)</pre>
      * where <code>F</code> is the F value and <code>cumulativeProbability</code>
-     * is the commons-math implementation of the F distribution.</p>
+     * is the commons-math implementation of the F distribution.
      *
      * @param categoryData <code>Collection</code> of {@link SummaryStatistics}
      * each containing data for one category
@@ -221,14 +221,14 @@ public class OneWayAnova {
      * <code>categoryData</code> collection and each of these arrays must
      * contain at least two values.</li>
      * <li>alpha must be strictly greater than 0 and less than or equal to 0.5.
-     * </li></ul></p><p>
+     * </li></ul><p>
      * This implementation uses the
      * {@link org.apache.commons.math4.distribution.FDistribution
      * commons-math F Distribution implementation} to estimate the exact
      * p-value, using the formula<pre>
      *   p = 1 - cumulativeProbability(F)</pre>
      * where <code>F</code> is the F value and <code>cumulativeProbability</code>
-     * is the commons-math implementation of the F distribution.</p>
+     * is the commons-math implementation of the F distribution.
      * <p>True is returned iff the estimated p-value is less than alpha.</p>
      *
      * @param categoryData <code>Collection</code> of <code>double[]</code>

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/TTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/TTest.java b/src/main/java/org/apache/commons/math4/stat/inference/TTest.java
index 577ac29..45bb9f3 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/TTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/TTest.java
@@ -40,7 +40,7 @@ import org.apache.commons.math4.util.FastMath;
  * <li>Homoscedastic (equal variance assumption) or heteroscedastic
  * (for two sample tests)</li>
  * <li>Fixed significance level (boolean-valued) or returning p-values.
- * </li></ul></p>
+ * </li></ul>
  * <p>
  * Test statistics are available for all tests.  Methods including "Test" in
  * in their names perform tests, all other methods return t-statistics.  Among
@@ -67,7 +67,7 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The input arrays must have the same length and their common length
      * must be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -115,7 +115,7 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The input array lengths must be the same and their common length must
      * be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -159,7 +159,7 @@ public class TTest {
      * must be at least 2.
      * </li>
      * <li> <code> 0 &lt; alpha &lt; 0.5 </code>
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -191,7 +191,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param mu comparison constant
      * @param observed array of values
@@ -218,7 +218,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li><code>observed.getN() &ge; 2</code>.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param mu comparison constant
      * @param sampleStats DescriptiveStatistics holding sample summary statitstics
@@ -250,8 +250,7 @@ public class TTest {
      * where <strong><code>n1</code></strong> is the size of first sample;
      * <strong><code> n2</code></strong> is the size of second sample;
      * <strong><code> m1</code></strong> is the mean of first sample;
-     * <strong><code> m2</code></strong> is the mean of second sample</li>
-     * </ul>
+     * <strong><code> m2</code></strong> is the mean of second sample
      * and <strong><code>var</code></strong> is the pooled variance estimate:
      * </p><p>
      * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code>
@@ -261,7 +260,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -302,7 +301,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -323,7 +322,7 @@ public class TTest {
     }
 
     /**
-     * Computes a 2-sample t statistic </a>, comparing the means of the datasets
+     * Computes a 2-sample t statistic, comparing the means of the datasets
      * described by two {@link StatisticalSummary} instances, without the
      * assumption of equal subpopulation variances.  Use
      * {@link #homoscedasticT(StatisticalSummary, StatisticalSummary)} to
@@ -346,7 +345,7 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sampleStats1 StatisticalSummary describing data from the first sample
      * @param sampleStats2 StatisticalSummary describing data from the second sample
@@ -394,7 +393,7 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sampleStats1 StatisticalSummary describing data from the first sample
      * @param sampleStats2 StatisticalSummary describing data from the second sample
@@ -432,7 +431,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param mu constant value to compare sample mean against
      * @param sample array of sample data values
@@ -465,11 +464,11 @@ public class TTest {
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
      * the 95% level, use <br><code>tTest(mu, sample, 0.05) </code>
      * </li>
-     * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
+     * <li>To test the (one-sided) hypothesis <code> sample mean &lt; mu </code>
      * at the 99% level, first verify that the measured sample mean is less
      * than <code>mu</code> and then use
      * <br><code>tTest(mu, sample, 0.02) </code>
-     * </li></ol></p>
+     * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the one-sample
@@ -478,7 +477,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param mu constant value to compare sample mean against
      * @param sample array of sample data values
@@ -518,7 +517,7 @@ public class TTest {
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The sample must contain at least 2 observations.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param mu constant value to compare sample mean against
      * @param sampleStats StatisticalSummary describing sample data
@@ -551,11 +550,11 @@ public class TTest {
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
      * the 95% level, use <br><code>tTest(mu, sampleStats, 0.05) </code>
      * </li>
-     * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
+     * <li>To test the (one-sided) hypothesis <code> sample mean &lt; mu </code>
      * at the 99% level, first verify that the measured sample mean is less
      * than <code>mu</code> and then use
      * <br><code>tTest(mu, sampleStats, 0.02) </code>
-     * </li></ol></p>
+     * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the one-sample
@@ -564,7 +563,7 @@ public class TTest {
      * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The sample must include at least 2 observations.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param mu constant value to compare sample mean against
      * @param sampleStats StatisticalSummary describing sample data values
@@ -613,7 +612,7 @@ public class TTest {
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -641,7 +640,7 @@ public class TTest {
      * comparing the means of the input arrays, under the assumption that
      * the two samples are drawn from subpopulations with equal variances.
      * To perform the test without the equal variances assumption, use
-     * {@link #tTest(double[], double[])}.</p>
+     * {@link #tTest(double[], double[])}.
      * <p>
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
@@ -660,7 +659,7 @@ public class TTest {
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -708,11 +707,11 @@ public class TTest {
      * the 95% level,  use
      * <br><code>tTest(sample1, sample2, 0.05). </code>
      * </li>
-     * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>,
+     * <li>To test the (one-sided) hypothesis <code> mean 1 &lt; mean 2 </code>,
      * at the 99% level, first verify that the measured  mean of <code>sample 1</code>
      * is less than the mean of <code>sample 2</code> and then use
      * <br><code>tTest(sample1, sample2, 0.02) </code>
-     * </li></ol></p>
+     * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
@@ -723,8 +722,8 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
      * </li>
-     * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul></p>
+     * <li> <code> 0 &lt; alpha &lt; 0.5 </code>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -770,12 +769,12 @@ public class TTest {
      * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at
      * the 95% level, use <br><code>tTest(sample1, sample2, 0.05). </code>
      * </li>
-     * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2, </code>
+     * <li>To test the (one-sided) hypothesis <code> mean 1 &lt; mean 2, </code>
      * at the 99% level, first verify that the measured mean of
      * <code>sample 1</code> is less than the mean of <code>sample 2</code>
      * and then use
      * <br><code>tTest(sample1, sample2, 0.02) </code>
-     * </li></ol></p>
+     * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
@@ -786,8 +785,8 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
      * </li>
-     * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul></p>
+     * <li> <code> 0 &lt; alpha &lt; 0.5 </code>
+     * </li></ul>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -835,7 +834,7 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sampleStats1  StatisticalSummary describing data from the first sample
      * @param sampleStats2  StatisticalSummary describing data from the second sample
@@ -882,7 +881,7 @@ public class TTest {
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param sampleStats1  StatisticalSummary describing data from the first sample
      * @param sampleStats2  StatisticalSummary describing data from the second sample
@@ -931,12 +930,12 @@ public class TTest {
      * the 95%, use
      * <br><code>tTest(sampleStats1, sampleStats2, 0.05) </code>
      * </li>
-     * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>
+     * <li>To test the (one-sided) hypothesis <code> mean 1 &lt; mean 2 </code>
      * at the 99% level,  first verify that the measured mean of
      * <code>sample 1</code> is less than  the mean of <code>sample 2</code>
      * and then use
      * <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
-     * </li></ol></p>
+     * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
@@ -948,8 +947,8 @@ public class TTest {
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
      * </li>
-     * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul></p>
+     * <li> <code> 0 &lt; alpha &lt; 0.5 </code>
+     * </li></ul>
      *
      * @param sampleStats1 StatisticalSummary describing sample data values
      * @param sampleStats2 StatisticalSummary describing sample data values

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java b/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java
index 537d1c4..4ffff61 100644
--- a/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java
+++ b/src/main/java/org/apache/commons/math4/stat/inference/WilcoxonSignedRankTest.java
@@ -158,7 +158,6 @@ public class WilcoxonSignedRankTest {
      * ordered, so the comparisons greater than, less than, and equal to are
      * meaningful.</li>
      * </ul>
-     * </p>
      *
      * @param x the first sample
      * @param y the second sample
@@ -281,13 +280,12 @@ public class WilcoxonSignedRankTest {
      * ordered, so the comparisons greater than, less than, and equal to are
      * meaningful.</li>
      * </ul>
-     * </p>
      *
      * @param x the first sample
      * @param y the second sample
      * @param exactPValue
-     *            if the exact p-value is wanted (only works for x.length <= 30,
-     *            if true and x.length > 30, this is ignored because
+     *            if the exact p-value is wanted (only works for x.length &gt;= 30,
+     *            if true and x.length &lt; 30, this is ignored because
      *            calculations may take too long)
      * @return p-value
      * @throws NullArgumentException if {@code x} or {@code y} are {@code null}.
@@ -295,7 +293,7 @@ public class WilcoxonSignedRankTest {
      * @throws DimensionMismatchException if {@code x} and {@code y} do not
      * have the same length.
      * @throws NumberIsTooLargeException if {@code exactPValue} is {@code true}
-     * and {@code x.length} > 30
+     * and {@code x.length} &gt; 30
      * @throws ConvergenceException if the p-value can not be computed due to
      * a convergence error
      * @throws MaxCountExceededException if the maximum number of iterations

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java b/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java
index 8639929..9bf9e23 100644
--- a/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java
+++ b/src/main/java/org/apache/commons/math4/stat/interval/BinomialConfidenceInterval.java
@@ -42,7 +42,6 @@ public interface BinomialConfidenceInterval {
      * <li>{@code numberOfSuccesses} may not exceed {@code numberOfTrials}</li>
      * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li>
      * </ul>
-     * </p>
      *
      * @param numberOfTrials number of trials
      * @param numberOfSuccesses number of successes

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java b/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java
index 4cadd1f..e41ebf1 100644
--- a/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java
+++ b/src/main/java/org/apache/commons/math4/stat/interval/ConfidenceInterval.java
@@ -46,7 +46,6 @@ public class ConfidenceInterval {
      * <li>{@code lower} must be strictly less than {@code upper}</li>
      * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li>
      * </ul>
-     * </p>
      *
      * @param lowerBound lower endpoint of the interval
      * @param upperBound upper endpoint of the interval

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java b/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java
index 86a7949..11ead0c 100644
--- a/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java
+++ b/src/main/java/org/apache/commons/math4/stat/interval/IntervalUtils.java
@@ -86,7 +86,6 @@ public final class IntervalUtils {
      * <li>{@code numberOfSuccesses} may not exceed {@code numberOfTrials}</li>
      * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li>
      * </ul>
-     * </p>
      *
      * @param numberOfTrials number of trials
      * @param numberOfSuccesses number of successes

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java b/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java
index ad15725..4d55867 100644
--- a/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java
+++ b/src/main/java/org/apache/commons/math4/stat/ranking/NaturalRanking.java
@@ -41,7 +41,7 @@ import org.apache.commons.math4.util.FastMath;
  * {@link UniformRandomProvider random generator} may be supplied as a
  * constructor argument.</p>
  * <p>Examples:
- * <table border="1" cellpadding="3">
+ * <table border="1" cellpadding="3" summary="Examples">
  * <tr><th colspan="3">
  * Input data: (20, 17, 30, 42.3, 17, 50, Double.NaN, Double.NEGATIVE_INFINITY, 17)
  * </th></tr>
@@ -66,7 +66,7 @@ import org.apache.commons.math4.util.FastMath;
  * <tr>
  * <td>MINIMAL</td>
  * <td>MAXIMUM</td>
- * <td>(6, 5, 7, 8, 5, 9, 2, 2, 5)</td></tr></table></p>
+ * <td>(6, 5, 7, 8, 5, 9, 2, 2, 5)</td></tr></table>
  *
  * @since 2.0
  */

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java
index 99775c4..d7036e3 100644
--- a/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java
+++ b/src/main/java/org/apache/commons/math4/stat/regression/AbstractMultipleLinearRegression.java
@@ -98,9 +98,8 @@ public abstract class AbstractMultipleLinearRegression implements
      * </p>
      * <p>Throws IllegalArgumentException if any of the following preconditions fail:
      * <ul><li><code>data</code> cannot be null</li>
-     * <li><code>data.length = nobs * (nvars + 1)</li>
+     * <li><code>data.length = nobs * (nvars + 1)</code></li>
      * <li><code>nobs > nvars</code></li></ul>
-     * </p>
      *
      * @param data input data array
      * @param nobs number of observations (rows)
@@ -171,7 +170,6 @@ public abstract class AbstractMultipleLinearRegression implements
      *     3    4
      *     5    6
      * </pre>
-     * </p>
      * <p>Note that there is no need to add an initial unitary column (column of 1's) when
      * specifying a model including an intercept term.
      * </p>

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java
index 4f421d1..abc8fea 100644
--- a/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java
+++ b/src/main/java/org/apache/commons/math4/stat/regression/MillerUpdatingRegression.java
@@ -33,7 +33,7 @@ import org.apache.commons.numbers.core.Precision;
  * Series C (Applied Statistics), Vol. 41, No. 2
  * (1992), pp. 458-478
  * Published by: Blackwell Publishing for the Royal Statistical Society
- * Stable URL: http://www.jstor.org/stable/2347583 </pre></p>
+ * Stable URL: http://www.jstor.org/stable/2347583 </pre>
  *
  * <p>This method for multiple regression forms the solution to the OLS problem
  * by updating the QR decomposition as described by Gentleman.</p>
@@ -596,7 +596,7 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
      *
      * <p>If IN = 0, the value returned in array CORMAT for the correlation
      * of variables Xi & Xj is: <pre>
-     * sum ( Xi.Xj ) / Sqrt ( sum (Xi^2) . sum (Xj^2) )</pre></p>
+     * sum ( Xi.Xj ) / Sqrt ( sum (Xi^2) . sum (Xj^2) )</pre>
      *
      * <p>On return, array CORMAT contains the upper triangle of the matrix of
      * partial correlations stored by rows, excluding the 1's on the diagonal.

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java
index 38b38c1..113a04f 100644
--- a/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java
+++ b/src/main/java/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.java
@@ -30,7 +30,7 @@ import org.apache.commons.math4.stat.descriptive.moment.SecondMoment;
  * multiple linear regression model.</p>
  *
  * <p>The regression coefficients, <code>b</code>, satisfy the normal equations:
- * <pre><code> X<sup>T</sup> X b = X<sup>T</sup> y </code></pre></p>
+ * <pre><code> X<sup>T</sup> X b = X<sup>T</sup> y </code></pre>
  *
  * <p>To solve the normal equations, this implementation uses QR decomposition
  * of the <code>X</code> matrix. (See {@link QRDecomposition} for details on the
@@ -45,7 +45,7 @@ import org.apache.commons.math4.stat.descriptive.moment.SecondMoment;
  * R<sup>T</sup> (Q<sup>T</sup>Q) R b = R<sup>T</sup> Q<sup>T</sup> y
  * R<sup>T</sup> R b = R<sup>T</sup> Q<sup>T</sup> y
  * (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> R b = (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> Q<sup>T</sup> y
- * R b = Q<sup>T</sup> y </code></pre></p>
+ * R b = Q<sup>T</sup> y </code></pre>
  *
  * <p>Given <code>Q</code> and <code>R</code>, the last equation is solved by back-substitution.</p>
  *
@@ -210,7 +210,7 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
      *
      * <p>If the regression is estimated without an intercept term, what is returned is <pre>
      * <code> 1 - (1 - {@link #calculateRSquared()}) * (n / (n - p)) </code>
-     * </pre></p>
+     * </pre>
      *
      * <p>If there is no variance in y, i.e., SSTO = 0, NaN is returned.</p>
      *

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java b/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java
index bc8f3c1..8d15d49 100644
--- a/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java
+++ b/src/main/java/org/apache/commons/math4/stat/regression/RegressionResults.java
@@ -300,7 +300,7 @@ public class RegressionResults implements Serializable {
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double.NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return sum of squared deviations of predicted y values
      */
@@ -322,7 +322,7 @@ public class RegressionResults implements Serializable {
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double,NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return sum of squared errors associated with the regression model
      */
@@ -354,7 +354,7 @@ public class RegressionResults implements Serializable {
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, {@code Double,NaN} is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return r-square, a double in the interval [0, 1]
      */
@@ -372,7 +372,7 @@ public class RegressionResults implements Serializable {
      *
      * <p>If the regression is estimated without an intercept term, what is returned is <pre>
      * <code> 1 - (1 - {@link #getRSquared()} ) * (n / (n - p)) </code>
-     * </pre></p>
+     * </pre>
      *
      * @return adjusted R-Squared statistic
      */

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java b/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java
index 55b0d44..201c172 100644
--- a/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java
+++ b/src/main/java/org/apache/commons/math4/stat/regression/SimpleRegression.java
@@ -57,7 +57,7 @@ import org.apache.commons.numbers.core.Precision;
  * the {@link #SimpleRegression(boolean)} constructor.  When the
  * {@code hasIntercept} property is false, the model is estimated without a
  * constant term and {@link #getIntercept()} returns {@code 0}.</li>
- * </ul></p>
+ * </ul>
  *
  */
 public class SimpleRegression implements Serializable, UpdatingMultipleLinearRegression {
@@ -370,7 +370,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double,NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param x input <code>x</code> value
      * @return predicted <code>y</code> value
@@ -396,7 +396,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double,NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return the intercept of the regression line if the model includes an
      * intercept; 0 otherwise
@@ -429,7 +429,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
     * must have been added before invoking this method. If this method is
     * invoked before a model can be estimated, <code>Double.NaN</code> is
     * returned.
-    * </li></ul></p>
+    * </li></ul>
     *
     * @return the slope of the regression line
     */
@@ -468,7 +468,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double,NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return sum of squared errors associated with the regression model
      */
@@ -496,7 +496,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
     /**
      * Returns the sum of squared deviations of the x values about their mean.
      *
-     * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p>
+     * If <code>n &lt; 2</code>, this returns <code>Double.NaN</code>.
      *
      * @return sum of squared deviations of x values
      */
@@ -528,7 +528,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double.NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return sum of squared deviations of predicted y values
      */
@@ -563,7 +563,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double,NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return Pearson's r
      */
@@ -586,7 +586,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * must have been added before invoking this method. If this method is
      * invoked before a model can be estimated, <code>Double,NaN</code> is
      * returned.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @return r-square
      */
@@ -681,7 +681,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg
      * </li>
      * <li><code>(0 < alpha < 1)</code>; otherwise an
      * <code>OutOfRangeException</code> is thrown.
-     * </li></ul></p>
+     * </li></ul>
      *
      * @param alpha the desired significance level
      * @return half-width of 95% confidence interval for the slope estimate

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java b/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java
index 35d724b..c2c4562 100644
--- a/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java
+++ b/src/main/java/org/apache/commons/math4/transform/FastCosineTransformer.java
@@ -50,7 +50,7 @@ import org.apache.commons.math4.util.FastMath;
  * data set x<sub>0</sub>, &hellip;, x<sub>N-1</sub> is equal to <em>half</em>
  * of the N first elements of the DFT of the extended data set
  * x<sub>0</sub><sup>&#35;</sup>, &hellip;, x<sub>2N-3</sub><sup>&#35;</sup>
- * <br/>
+ * <br>
  * y<sub>n</sub> = (1 / 2) &sum;<sub>k=0</sub><sup>2N-3</sup>
  * x<sub>k</sub><sup>&#35;</sup> exp[-2&pi;i nk / (2N - 2)]
  * &nbsp;&nbsp;&nbsp;&nbsp;k = 0, &hellip;, N-1.

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java b/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java
index 1aafada..400cb9f 100644
--- a/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java
+++ b/src/main/java/org/apache/commons/math4/transform/FastHadamardTransformer.java
@@ -95,8 +95,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable {
      * <li><b>y</b> is the output vector (Fast Hadamard transform of <b>x</b>),</li>
      * <li>a and b are helper rows.</li>
      * </ol>
-     * <table align="center" border="1" cellpadding="3">
-     * <tbody align="center">
+     * <table style="text-align: center" border="1" cellpadding="3" summary="manual calculation for N=8">
+     * <tbody style="text-align: center">
      * <tr>
      *     <th>x</th>
      *     <th>a</th>
@@ -157,7 +157,7 @@ public class FastHadamardTransformer implements RealTransformer, Serializable {
      * <h3>How it works</h3>
      * <ol>
      * <li>Construct a matrix with {@code N} rows and {@code n + 1} columns,
-     * {@code hadm[n+1][N]}.<br/>
+     * {@code hadm[n+1][N]}.<br>
      * <em>(If I use [x][y] it always means [row-offset][column-offset] of a
      * Matrix with n rows and m columns. Its entries go from M[0][0]
      * to M[n][N])</em></li>
@@ -187,8 +187,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable {
      * <li><em>Algorithm from <a href="http://www.archive.chipcenter.com/dsp/DSP000517F1.html">chipcenter</a>.</em></li>
      * </ol>
      * <h3>Visually</h3>
-     * <table border="1" align="center" cellpadding="3">
-     * <tbody align="center">
+     * <table border="1" cellpadding="3" style="text-align: center" summary="chipcenter algorithm">
+     * <tbody style="text-align: center">
      * <tr>
      *     <td></td><th>0</th><th>1</th><th>2</th><th>3</th>
      *     <th>&hellip;</th>
@@ -198,8 +198,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable {
      *     <th>0</th>
      *     <td>x<sub>0</sub></td>
      *     <td colspan="5" rowspan="5" align="center" valign="middle">
-     *         &uarr;<br/>
-     *         &larr; D<sub>top</sub> &rarr;<br/>
+     *         &uarr;<br>
+     *         &larr; D<sub>top</sub> &rarr;<br>
      *         &darr;
      *     </td>
      * </tr>
@@ -211,8 +211,8 @@ public class FastHadamardTransformer implements RealTransformer, Serializable {
      *     <th>N / 2</th>
      *     <td>x<sub>N/2</sub></td>
      *     <td colspan="5" rowspan="5" align="center" valign="middle">
-     *         &uarr;<br/>
-     *         &larr; D<sub>bottom</sub> &rarr;<br/>
+     *         &uarr;<br>
+     *         &larr; D<sub>bottom</sub> &rarr;<br>
      *         &darr;
      *     </td>
      * </tr>

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java b/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java
index 71e2cfb..b4b27ec 100644
--- a/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java
+++ b/src/main/java/org/apache/commons/math4/transform/FastSineTransformer.java
@@ -53,7 +53,7 @@ import org.apache.commons.math4.util.FastMath;
  * data set x<sub>0</sub>, &hellip;, x<sub>N-1</sub> is equal to <em>half</em>
  * of i (the pure imaginary number) times the N first elements of the DFT of the
  * extended data set x<sub>0</sub><sup>&#35;</sup>, &hellip;,
- * x<sub>2N-1</sub><sup>&#35;</sup> <br />
+ * x<sub>2N-1</sub><sup>&#35;</sup> <br>
  * y<sub>n</sub> = (i / 2) &sum;<sub>k=0</sub><sup>2N-1</sup>
  * x<sub>k</sub><sup>&#35;</sup> exp[-2&pi;i nk / (2N)]
  * &nbsp;&nbsp;&nbsp;&nbsp;k = 0, &hellip;, N-1.

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/Combinations.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/util/Combinations.java b/src/main/java/org/apache/commons/math4/util/Combinations.java
index bf8a423..b67e50c 100644
--- a/src/main/java/org/apache/commons/math4/util/Combinations.java
+++ b/src/main/java/org/apache/commons/math4/util/Combinations.java
@@ -62,7 +62,7 @@ public class Combinations implements Iterable<int[]> {
      * For example, {@code new Combinations(4, 2).iterator()} returns
      * an iterator that will generate the following sequence of arrays
      * on successive calls to
-     * {@code next()}:<br/>
+     * {@code next()}:<br>
      * {@code [0, 1], [0, 2], [1, 2], [0, 3], [1, 3], [2, 3]}
      * </p>
      * If {@code k == 0} an iterator containing an empty array is returned;
@@ -90,7 +90,7 @@ public class Combinations implements Iterable<int[]> {
      * For example, {@code new Combinations(4, 2).iterator()} returns
      * an iterator that will generate the following sequence of arrays
      * on successive calls to
-     * {@code next()}:<br/>
+     * {@code next()}:<br>
      * {@code [0, 1], [0, 2], [1, 2], [0, 3], [1, 3], [2, 3]}
      * </p>
      * If {@code k == 0} an iterator containing an empty array is returned;

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java b/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java
index 6645ed9..a7c40b7 100644
--- a/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java
+++ b/src/main/java/org/apache/commons/math4/util/CombinatoricsUtils.java
@@ -75,7 +75,7 @@ public final class CombinatoricsUtils {
      * {@code  < Long.MAX_VALUE} is 66. If the computed value exceeds
      * {@code Long.MAX_VALUE} a {@code MathArithMeticException} is
      * thrown.</li>
-     * </ul></p>
+     * </ul>
      *
      * @param n the size of the set
      * @param k the size of the subsets to be counted
@@ -153,10 +153,10 @@ public final class CombinatoricsUtils {
      * <li> {@code 0 <= k <= n } (otherwise
      * {@code IllegalArgumentException} is thrown)</li>
      * <li> The result is small enough to fit into a {@code double}. The
-     * largest value of {@code n} for which all coefficients are <
+     * largest value of {@code n} for which all coefficients are &lt;
      * Double.MAX_VALUE is 1029. If the computed value exceeds Double.MAX_VALUE,
      * Double.POSITIVE_INFINITY is returned</li>
-     * </ul></p>
+     * </ul>
      *
      * @param n the size of the set
      * @param k the size of the subsets to be counted
@@ -201,7 +201,7 @@ public final class CombinatoricsUtils {
      * <ul>
      * <li> {@code 0 <= k <= n } (otherwise
      * {@code MathIllegalArgumentException} is thrown)</li>
-     * </ul></p>
+     * </ul>
      *
      * @param n the size of the set
      * @param k the size of the subsets to be counted
@@ -273,7 +273,6 @@ public final class CombinatoricsUtils {
      * Long.MAX_VALUE} is 20. If the computed value exceeds {@code Long.MAX_VALUE}
      * an {@code MathArithMeticException } is thrown.</li>
      * </ul>
-     * </p>
      *
      * @param n argument
      * @return {@code n!}

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java b/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java
index acd4b2c..536638e 100644
--- a/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java
+++ b/src/main/java/org/apache/commons/math4/util/ContinuedFraction.java
@@ -31,7 +31,6 @@ import org.apache.commons.math4.exception.util.LocalizedFormats;
  * <li><a href="http://mathworld.wolfram.com/ContinuedFraction.html">
  * Continued Fraction</a></li>
  * </ul>
- * </p>
  *
  */
 public abstract class ContinuedFraction {
@@ -111,7 +110,6 @@ public abstract class ContinuedFraction {
      * </ul>
      * <b>Note:</b> the implementation uses the terms a<sub>i</sub> and b<sub>i</sub> as defined in
      * <a href="http://mathworld.wolfram.com/ContinuedFraction.html">Continued Fraction @ MathWorld</a>.
-     * </p>
      *
      * @param x the evaluation point.
      * @param epsilon maximum error allowed.

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/FastMath.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/util/FastMath.java b/src/main/java/org/apache/commons/math4/util/FastMath.java
index 20a9527..aaa69be 100644
--- a/src/main/java/org/apache/commons/math4/util/FastMath.java
+++ b/src/main/java/org/apache/commons/math4/util/FastMath.java
@@ -77,7 +77,6 @@ import org.apache.commons.math4.exception.util.LocalizedFormats;
  * <li>{@link #nextUp(float)}</li>
  * <li>{@link #scalb(float, int)}</li>
  * </ul>
- * </p>
  * @since 2.2
  */
 public class FastMath {
@@ -3334,6 +3333,7 @@ public class FastMath {
      * <li>+MAX_VALUE</li>
      * <li>+INFINITY</li>
      * <li></li>
+     * </ul>
      * <p>
      * If arguments compare equal, then the second argument is returned.
      * <p>
@@ -3390,6 +3390,7 @@ public class FastMath {
      * <li>+MAX_VALUE</li>
      * <li>+INFINITY</li>
      * <li></li>
+     * </ul>
      * <p>
      * If arguments compare equal, then the second argument is returned.
      * <p>
@@ -3433,7 +3434,7 @@ public class FastMath {
 
     /** Get the largest whole number smaller than x.
      * @param x number from which floor is requested
-     * @return a double number f such that f is an integer f <= x < f + 1.0
+     * @return a double number f such that f is an integer f &lt;= x &lt; f + 1.0
      */
     public static double floor(double x) {
         long y;
@@ -3460,7 +3461,7 @@ public class FastMath {
 
     /** Get the smallest whole number larger than x.
      * @param x number from which ceil is requested
-     * @return a double number c such that c is an integer c - 1.0 < x <= c
+     * @return a double number c such that c is an integer c - 1.0 &lt; x &lt;= c
      */
     public static double ceil(double x) {
         double y;
@@ -3485,7 +3486,7 @@ public class FastMath {
 
     /** Get the whole number that is the nearest to x, or the even one if x is exactly half way between two integers.
      * @param x number from which nearest whole number is requested
-     * @return a double number r such that r is an integer r - 0.5 <= x <= r + 0.5
+     * @return a double number r such that r is an integer r - 0.5 &lt;= x &lt;= r + 0.5
      */
     public static double rint(double x) {
         double y = floor(x);
@@ -3696,7 +3697,7 @@ public class FastMath {
 
     /**
      * Returns the hypotenuse of a triangle with sides {@code x} and {@code y}
-     * - sqrt(<i>x</i><sup>2</sup>&nbsp;+<i>y</i><sup>2</sup>)<br/>
+     * - sqrt(<i>x</i><sup>2</sup>&nbsp;+<i>y</i><sup>2</sup>)<br>
      * avoiding intermediate overflow or underflow.
      *
      * <ul>
@@ -3750,7 +3751,6 @@ public class FastMath {
      * of the quotient {@code x/y}.
      * If two mathematical integers are equally close to {@code x/y} then
      * {@code n} is the integer that is even.
-     * <p>
      * <ul>
      * <li>If either operand is NaN, the result is NaN.</li>
      * <li>If the result is not NaN, the sign of the result equals the sign of the dividend.</li>
@@ -3971,7 +3971,7 @@ public class FastMath {
             return a * b;
     }
 
-    /** Finds q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0.
+    /** Finds q such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0.
      * <p>
      * This methods returns the same value as integer division when
      * a and b are same signs, but returns a different value when
@@ -3979,7 +3979,7 @@ public class FastMath {
      * </p>
      * @param a dividend
      * @param b divisor
-     * @return q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0
+     * @return q such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0
      * @exception MathArithmeticException if b == 0
      * @see #floorMod(int, int)
      * @since 3.4
@@ -4001,7 +4001,7 @@ public class FastMath {
 
     }
 
-    /** Finds q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0.
+    /** Finds q such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0.
      * <p>
      * This methods returns the same value as integer division when
      * a and b are same signs, but returns a different value when
@@ -4009,7 +4009,7 @@ public class FastMath {
      * </p>
      * @param a dividend
      * @param b divisor
-     * @return q such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0
+     * @return q such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0
      * @exception MathArithmeticException if b == 0
      * @see #floorMod(long, long)
      * @since 3.4
@@ -4031,7 +4031,7 @@ public class FastMath {
 
     }
 
-    /** Finds r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0.
+    /** Finds r such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0.
      * <p>
      * This methods returns the same value as integer modulo when
      * a and b are same signs, but returns a different value when
@@ -4039,7 +4039,7 @@ public class FastMath {
      * </p>
      * @param a dividend
      * @param b divisor
-     * @return r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0
+     * @return r such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0
      * @exception MathArithmeticException if b == 0
      * @see #floorDiv(int, int)
      * @since 3.4
@@ -4061,7 +4061,7 @@ public class FastMath {
 
     }
 
-    /** Finds r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0.
+    /** Finds r such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0.
      * <p>
      * This methods returns the same value as integer modulo when
      * a and b are same signs, but returns a different value when
@@ -4069,7 +4069,7 @@ public class FastMath {
      * </p>
      * @param a dividend
      * @param b divisor
-     * @return r such that a = q b + r with 0 <= r < b if b > 0 and b < r <= 0 if b < 0
+     * @return r such that a = q b + r with 0 &lt;= r &lt; b if b &gt; 0 and b &lt; r &lt;= 0 if b &lt; 0
      * @exception MathArithmeticException if b == 0
      * @see #floorDiv(long, long)
      * @since 3.4

http://git-wip-us.apache.org/repos/asf/commons-math/blob/53ec46ba/src/main/java/org/apache/commons/math4/util/IntegerSequence.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/util/IntegerSequence.java b/src/main/java/org/apache/commons/math4/util/IntegerSequence.java
index 0ea1949..4712a4b 100644
--- a/src/main/java/org/apache/commons/math4/util/IntegerSequence.java
+++ b/src/main/java/org/apache/commons/math4/util/IntegerSequence.java
@@ -48,10 +48,10 @@ public class IntegerSequence {
     }
 
     /**
-     * Creates a sequence <code>a<sub>i</sub>, i < 0 < n</code>
+     * Creates a sequence <code>a<sub>i</sub>, i &lt; 0 &lt; n</code>
      * where <code>a<sub>i</sub> = start + i * step</code>
-     * and {@code n} is such that <code>a<sub>n</sub> <= max</code>
-     * and  <code>a<sub>n+1</sub> > max</code>.
+     * and {@code n} is such that <code>a<sub>n</sub> &lt;= max</code>
+     * and  <code>a<sub>n+1</sub> &gt; max</code>.
      *
      * @param start First value of the range.
      * @param max Last value of the range that satisfies the above
@@ -79,10 +79,10 @@ public class IntegerSequence {
         private final int step;
 
         /**
-         * Creates a sequence <code>a<sub>i</sub>, i < 0 < n</code>
+         * Creates a sequence <code>a<sub>i</sub>, i &lt; 0 &lt; n</code>
          * where <code>a<sub>i</sub> = start + i * step</code>
-         * and {@code n} is such that <code>a<sub>n</sub> <= max</code>
-         * and  <code>a<sub>n+1</sub> > max</code>.
+         * and {@code n} is such that <code>a<sub>n</sub> &lt;= max</code>
+         * and  <code>a<sub>n+1</sub> &gt; max</code>.
          *
          * @param start First value of the range.
          * @param max Last value of the range that satisfies the above
@@ -359,7 +359,7 @@ public class IntegerSequence {
         /**
          * Not applicable.
          *
-         * @throws MathUnsupportedOperationException
+         * @throws MathUnsupportedOperationException always
          */
         @Override
         public void remove() {


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