Multivariate Nonparametric Methods
Stratified Multivariate Kawaguchi Koch Wang Estimators
Calculate the quantiles of the count of concordant pairs among indpend...
Compare the sensitivity of different statistics.
Nonparametric Confidence Region for a Vector Shift Parameter
Noncentrality Parameter for a Given Level and Power
Generalization of Wilcoxon signed rank test
Perform the Terpstra version of the multi-ordered-sample test
Power for the nonparametric Terpstra test for an ordered effect.
Pairwise probabilities of Exceedence
Calculate the cumulative distribution of the count of concordant pairs...
Power Plot
Derivative of pairwise probabilities of Exceedence
One-way ANOVA using permutation tests
Permutation test of assication
Calculate the probability atom of the count of concordant pairs among ...
Mann Whitney Probability Mass function
Confidence Intervals for Empirical Cumulative Distribution Functions
Exact Quantile Confidence Interval
Normal-theory two sample scorestatistic.
Fisher's LSD method applied to the Kruskal-Wallis test
Sample Size for the Kruskal-Wallis test.
Power for the Kruskal-Wallis test.
Sample Size for the Kruskal-Wallis test.
Perform the Mann Whitney two-sample test
Mood's Median test, extended to odd sample sizes.
MultNonParam
Next permutation
Perform Page test for unbalanced two-way design
Diagnosis for multivariate stratified Kawaguchi - Koch - Wang method
Perform the Theil nonparametric estimation and confidence interval for...
Tukey HSD procedure
Two Sample Omnibus Tests of Survival Curves
Two Sample Omnibus Tests of Survival Curves
Plot a curve, skipping bits where there is a large jump.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.