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>.

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.

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.

Multivariate analysis methods including principal component analysis,
    partial least square regression, and multiblock analysis to describe,
    summarize, and visualize data with a group structure.

Two method new of multigroup and simulation of data.
  The first technique called multigroup PCA (mgPCA) this multivariate exploration approach
  that has the idea of considering the structure of groups and / or different types
  of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality
  Reduction (MDR) it is another multivariate exploration method that is based on
  projections. In addition, a method called Single Dimension Exploration (SDE) was
  incorporated for to analyze the exploration of the data. It could help us in a better
  way to observe the behavior of the multigroup data with certain variables of interest.