R/MANOVA.RM-package.R

In MANOVA.RM: Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs

#'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
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