Given a multivariate dataset and some knowledge about the dependencies between its features, it is important to ensure the observations or individuals are exchangeable before fitting a model to the data in order to make inferences from it, or assigning randomized treatments in order to estimate treatment effects. This package provides a flexible non-parametric test of exchangeability, allowing the user to specify the feature dependencies by hand. It can be used directly to evaluate whether a sample is exchangeable, and can also be piped into larger procedures that require exchangeable samples as outputs (e.g., clustering or community detection). See Aw, Spence and Song (2021+) for the accompanying paper.

It implements univariate and multivariate permutation test, also allowing multiplicity control.

methods, foreach, e1071, someMTP, cherry
It implements univariate and multivariate permutation
test, also allowing for multiplicity control.

Livio Finos, with contributions by Matteo Favero
It implements many univariate and multivariate permutation
(and rotation) tests. Allowed tests: the t one and two samples,
ANOVA, linear models, Chi Squared test, rank tests (i.e.
Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and McNemar.
Test on Linear Models are performed also in presence of
covariates (i.e. nuisance parameters). The permutation and the
rotation methods to get the null distribution of the test
statistic(s) are available. It also implements methods for
multiplicity control such as Westfall-Young min-p procedure and
Closed Testing (Marcus, 1976).

R (>= 2.15.1), methods, foreach, e1071, someMTP, cherry

Livio Finos, with contributions by Dario Basso, Aldo Solari and
Jelle Goeman
It implements many univariate and multivariate permutation
(and rotation) tests. Allowed tests: the t one and two samples,
ANOVA, linear models, Chi Squared test, rank tests (i.e.
Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and McNemar.
Test on Linear Models are performed also in presence of
covariates (i.e. nuisance parameters). The permutation and the
rotation methods to get the null distribution of the test
statistics are available. It also implements methods for
multiplicity control such as Westfall-Young minP procedure and
Closed Testing (Marcus, 1976). Moreover, it allows to test for
fixed effects in mixed effects models.