A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) <doi:10.1016/j.brat.2022.104072>. Since November 2025, this package contains code from the 'ez' R package (Copyright (c) 2016-11-01, Michael A. Lawrence <mike.lwrnc@gmail.com>), originally distributed under the 'GPL' (equal and above 2) license.

Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.

Detecting multiple disease clusters using the
    information criterion and scan statistic approach developed by
    Takahashi and Shimadzu (2020) <doi:10.1186/s12942-020-00228-y>.

Methods for the analysis of how ecological drivers affect the
    multifunctionality of an ecosystem based on methods of Byrnes et al. 
    2016 <doi:10.1111/2041-210X.12143> and Byrnes et al. 
    2022 <doi:10.1101/2022.03.17.484802>. Most standard
    methods in the literature are implemented (see vignettes) in a tidy
    format.

Read a table of fixed width formatted data of different types into
    a data.frame for each type.

Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2022), <arXiv:2208.14721>.

Functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.

Methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.