Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series.

Extends 'multiverse' package
    (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021)
    <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable
    multiverse analysis in R. This extension provides an additional level of
    abstraction to the 'multiverse' package with the aim of creating
    user friendly syntax to researchers, educators, and students in statistics.
    The 'mverse' syntax is designed to allow piping and takes hints from
    the 'tidyverse' grammar. The package allows users to define and inspect
    multiverse analysis using familiar syntax in R.

Multivariate estimation and testing, currently a package for testing parametric data. To deal with parametric data, various multivariate normality tests and outlier detection are performed and visualized using the 'ggplot2' package. Homogeneity tests for covariance matrices are also possible, as well as the Hotelling's T-square test and the multivariate analysis of variance test. We are exploring additional tests and visualization techniques, such as profile analysis and randomized complete block design, to be made available in the future and making them easily accessible to users.