Various Methods for the Goodness-of-Fit Problem in D>1 Dimensions R package MDgof version 1.0.1 The routine gof_test() in this package runs the goodness-of-fit test using various test statistic for multivariate data. Models under the null hypothesis can either be simple or allow for parameter estimation. p values are found via the parametric bootstrap (simulation). The routine gof_test_adjusted_pvalues() runs several tests and then finds a p value adjusted for simultaneous inference. The routine gof_power() allows the estimation of the power of the tests. hybrid_test() and hybrid_power() do the same by first generating a Monte Carlo data set under the null hypothesis and then running a number of two-sample methods. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package via a large number of case studies. For details of the methods and references see the included vignettes. Software Depends: R (>= 3.5) Imports: Rcpp, parallel, stats, microbenchmark, spatstat.geom, spatstat.explore, FNN, copula, mvtnorm, ggplot2, microbenchmark, MD2sample LinkingTo: Rcpp Suggests: rmarkdown, knitr Wolfgang Rolke <wolfgang.rolke@upr.edu> Comprehensive R Archive Network (CRAN) Wolfgang Rolke [aut, cre] (ORCID: <https://orcid.org/0000-0002-3514-726X>) GPL (>= 2) 2026-06-03 application/tgz https://CRAN.R-project.org/package=MDgof doi:10.32614/CRAN.package.MDgof