Name | Description | Type | Package | Framework |
ADFAsymptoticDistribution | This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test There are three main versions of the test and thus three possible asymptotic distributions: | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFAsymptoticDistribution1 | This is the asymptotic distribution of the Augmented Dickey-Fuller test statistic, for the TrendType. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFAsymptoticDistribution1 .Type | the available types of Dickey-Fuller teststhe augmented version of the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFDistribution | This represents an Augmented Dickey Fuller distribution. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFDistributionTable | A table contains the simulated observations/values of an empirical ADF distribution for a given set of parameters. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFDistributionTable_CONSTANT_lag0 | This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFDistributionTable_CONSTANT_TIME_lag0 | This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFDistributionTable_NO_CONSTANT_lag0 | This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFFiniteSampleDistribution | This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test There are three main versions of the test and thus three possible asymptotic distributions: | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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AndersonDarling | This algorithm calculates the Anderson-Darling k-sample test statistics and p-values. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
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AndersonDarlingPValue | This algorithm calculates the p-value when the Anderson-Darling statistic and the number of samples are given. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
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AS159 | Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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AS159 .RandomMatrix | | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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AugmentedDickeyFuller | The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time That is, whether the series has a unit root. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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Bartlett | Bartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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BoxPierce | The Box-Pierce test (named for George E. | Class | com.numericalmethod.suanshu.stats.test.timeseries.portmanteau | SuanShu |
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BreuschPagan | The Breusch-Pagan test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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BrownForsythe | The Brown-Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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ChiSquareIndependenceTest | Pearson's chi-square test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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ChiSquareIndependenceTest .Type | the available distributions used for the testThe default is the asymptotic distribution of Fisher's exact test. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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CramerVonMises2Samples | This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
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DAgostino | D'Agostino's K2 test is a goodness-of-fit measure of departure from normality. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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F | The F-test tests whether two normal populations have the same variance. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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FisherExactDistribution | Fisher's exact test distribution is, as its name states, exact, and can therefore be used regardless of the sample characteristics. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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Glejser | The Glejser test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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HarveyGodfrey | The Harvey-Godfrey test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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Heteroskedasticity | A heteroskedasticity test tests, for a linear regression model, whether the estimated variance of the residuals from a regression is dependent on the values of the independent variables (regressors). | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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HypothesisTest | A statistical hypothesis test is a method of making decisions using experimental data. | Class | com.numericalmethod.suanshu.stats.test | SuanShu |
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JarqueBera | The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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JarqueBeraDistribution | Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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KolmogorovDistribution | The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovOneSidedDistribution | | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov | The Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test). | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov .Side | the available types of the Kolmogorov-Smirnov statistic check whether the cdf of a sample lies above the null hypothesis | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov .Type | the available types of the Kolmogorov-Smirnov teststhe one-sample Kolmogorov-Smirnov test | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov1Sample | The one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov2Samples | The two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovTwoSamplesDistribution | Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovTwoSamplesDistribution .Side | the available types of Kolmogorov-Smirnov two-sample testtwo-sample; two-sided | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KruskalWallis | The Kruskal-Wallis test is a non-parametric method for testing the equality of population medians among groups. | Class | com.numericalmethod.suanshu.stats.test.rank | SuanShu |
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Levene | The Levene test tests for the equality of variance of groups. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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Levene .Type | the available implementations when computing the absolute deviationscompute the absolute | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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Lilliefors | Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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LjungBox | The Ljung-Box test (named for Greta M. | Class | com.numericalmethod.suanshu.stats.test.timeseries.portmanteau | SuanShu |
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OneWayANOVA | The One-Way ANOVA test tests for the equality of the means of several groups. | Class | com.numericalmethod.suanshu.stats.test.mean | SuanShu |
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ShapiroWilk | The Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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ShapiroWilkDistribution | Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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SiegelTukey | The Siegel-Tukey test tests for differences in scale (variability) between two groups. | Class | com.numericalmethod.suanshu.stats.test.rank | SuanShu |
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T | Student's t-test tests for the equality of means, for the one-sample case, against a hypothetical mean, | Class | com.numericalmethod.suanshu.stats.test.mean | SuanShu |
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TrendType | These are the three versions of the Augmented Dickey-Fuller (ADF) test. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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VanDerWaerden | The Van der Waerden test tests for the equality of all population distribution functions. | Class | com.numericalmethod.suanshu.stats.test.rank | SuanShu |
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White | The White test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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WilcoxonRankSum | The Wilcoxon rank sum test tests for the equality of means of two populations, or whether the means differ by an offset. | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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WilcoxonRankSumDistribution | Compute the exact distribution of the Wilcoxon rank sum test statistic. | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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WilcoxonSignedRank | The Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, and | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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WilcoxonSignedRankDistribution | Compute the exact distribution of the Wilcoxon signed rank test statistic. | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |