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.

Generates multivariate subgaussian stable probabilities using the QRSVN
    algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394>
    but by sampling positive stable variates not chi/sqrt(nu).

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>,
    Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by 
	Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.