#'MANOVA.RM: A package for calculating test statistics and their resampling versions for
#'heteroscedastic semi-parametric multivariate data or repeated measures designs.
#'
#'The MANOVA.RM package provides three important functions: MANOVA(), RM() and multRM() which
#'will be explained in detail below.
#'
#'@section MANOVA and MANOVA.wide function: The MANOVA() and MANOVA.wide() functions provide
#' the Wald-type statistic (WTS) as well as a modified ANOVA-type statistic (MATS)
#' as in Friedrich and Pauly (2018)
#' for multivariate designs with metric data as described in
#' Konietschke et al. (2015). These are applicable
#' for non-normal error terms, different sample sizes and/or
#' heteroscedastic variances. The MATS can even handle designs involving singular
#' covariance matrices. The tests are implemented for designs with an arbitrary
#' number of crossed factors or for nested designs. In addition to the
#' asymptotic p-values, they also provide p-values based on resampling
#' approaches (parametric or wild bootstrap). The difference between the two functions
#' is the format of the data: For MANOVA(), the data needs to be in long format,
#' while MANOVA.wide() is for data in wide format.
#' For further details, see \code{MANOVA} and \code{MANOVA.wide}.
#'
#'@section RM function: The RM() function provides the Wald-type
#' statistic (WTS) as well as the ANOVA-type statistic (ATS) for repeated measures designs
#' with metric data as described in Friedrich et al. (2017).
#' These are even applicable for non-normal error terms and/or heteroscedastic
#' variances. It is implemented for designs with an arbitrary number of
#' whole-plot and sub-plot factors and allows for different sample sizes. In
#' addition to the asymptotic p-values, it also provides p-values based on
#' resampling approaches (Permutation, parametric bootstrap, Wild bootstrap).
#' For further details, see \code{RM}.
#'
#'@section multRM function: The multRM() function is a combination of the procedures
#' above suited for multivariate repeated measures designs. It provides the WTS and the MATS
#' along with p-values based on a parametric or a wild bootstrap approach.
#'
#'@references Friedrich, S., Konietschke, F., and Pauly, M. (2019). Resampling-Based Analysis
#' of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM.
#' The R Journal, 11(2), 380-400.
#'
#'Konietschke, F., Bathke, A. C., Harrar, S. W. and Pauly, M. (2015).
#' Parametric and nonparametric bootstrap methods for general MANOVA. Journal
#' of Multivariate Analysis, 140, 291-301.
#'
#' Friedrich, S., Brunner, E. and Pauly, M. (2017). Permuting longitudinal data
#' in spite of the dependencies. Journal of Multivariate Analysis, 153, 255-265.
#'
#' Friedrich, S., Konietschke, F., Pauly, M. (2016). GFD - An
#' R-package for the Analysis of General Factorial Designs.
#' Journal of Statistical Software, 79(1), 1-18.
#'
#' Bathke, A., Friedrich, S., Konietschke, F., Pauly, M., Staffen, W., Strobl, N. and
#' Hoeller, Y. (2018). Testing Mean Differences among Groups: Multivariate and Repeated
#' Measures Analysis with Minimal Assumptions. Multivariate Behavioral Research.
#' Doi: 10.1080/00273171.2018.1446320.
#'
#' Friedrich, S., and Pauly, M. (2018). MATS: Inference for potentially singular and
#' heteroscedastic MANOVA. Journal of Multivariate Analysis, 165, 166-179.
#'
#'@docType package
#'@name MANOVARM
#'@aliases MANOVA.RM-package
NULL
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