Package: MDgof 1.2.1

MDgof: Various Methods for the Goodness-of-Fit Problem in D>1 Dimensions

Provides multivariate goodness-of-fit testing with a common interface for several test statistics. Null models may be simple or include parameter estimation, with p-values obtained by parametric bootstrap simulation. The function gof_test_adjusted_pvalue() combines several tests and computes a p-value adjusted for simultaneous inference. The function gof_power() estimates test power. The functions hybrid_test() and hybrid_power() use Monte Carlo samples under the null together with two-sample procedures. The function run.studies() supports systematic power comparisons of user-supplied and included methods across case studies. See the included vignettes for method details and references.

Authors:Wolfgang Rolke [aut, cre]

MDgof_1.2.1.tar.gz
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MDgof_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MDgof/json (API)

# Install 'MDgof' in R:
install.packages('MDgof', repos = c('https://wolfgangrolke.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.38 score 670 downloads 24 exports 63 dependencies

Last updated from:7d7531d717. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK388
linux-devel-x86_64OK202
source / vignettesOK237
linux-release-arm64OK165
linux-release-x86_64OK221
macos-release-arm64OK174
macos-release-x86_64OK478
macos-oldrel-arm64OK167
macos-oldrel-x86_64OK418
windows-devel-arm64OK401
windows-devel-x86_64OK220
windows-release-arm64OK307
windows-release-x86_64OK208
windows-oldrel-x86_64OK128
wasm-releaseOK151

Exports:case.studiescase.studies.contcase.studies.cont.D5case.studies.disccase.studies.estchange.marginalschi_cont_testchi_disc_testchi_powerdiscretizedraw_casegen.copgof_powergof_testgof_test_adjusted_pvaluehybrid_powerhybrid_testnewTSp2dCRipleyKrun.studiessq2recTS_contTS_disc

Dependencies:abindade4ADGofTestBallcliclustercodetoolscolorspacecopulacpp11deldirfarverFNNforeachgamggplot2gluegoftestgslgtablegTestsigraphisobanditeratorslabelinglatticelifecyclelsamagrittrMASSMatrixMD2samplemicrobenchmarkmvtnormnlmenumDerivpcaPPpixmappkgconfigpolyclippsplineR6RColorBrewerRcppRcppArmadillorlangS7scalesSnowballCspspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstabledistsurvivaltensorvctrsviridisLitewithr

MDgof-Case-Studies
Continuous Data, dim=2, equal marginals, no parameter estimation | Continuous Data, dim=2, unequal marginals, no parameter estimation | Continuous Data, dim=2, equal marginals, with parameter estimation | Continuous Data, dim=2, unequal marginals, with parameter estimation | Continuous Data, dim=5, equal marginals, no parameter estimation | Continuous Data, dim=5, unequal marginals, no parameter estimation | Discrete data
Rendered fromMDgof-Case-Studies.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2026-02-12

MDgof-Examples
Example 1: Continuous data without parameter estimation | Example 2: Continuous data with parameter estimation | New Tests | Example 3 | Adjusted p values | Power Estimation | Example 4 | Discrete Data | Example 5 | Example 6 | User supplied tests | Example 7 | Benchmarking | Example 8
Rendered fromMDgof-Examples.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2026-02-12

MDgof-hybrid
Example 1: Continuous data without parameter estimation | Example 2: Continuous data with parameter estimation | New Tests | Example 3 | Power Estimation | Example 4 | Discrete Data | Example 5 | User supplied tests | Example 7
Rendered fromMDgof-hybrid.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2026-02-12

MDgof-Methods
Continuous data | Tests based on a comparison of the theoretical and the empirical distribution function. | Tests based on the Rosenblatt transform. | Discrete data | Methods based on the empirical distribution fuction. | Methods based on the density | References
Rendered fromMDgof-Methods.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2026-02-12

Readme and manuals

Help Manual

Help pageTopics
Convert an MDgof power result to a data frameas.data.frame.MDgof_power
Run Bakshaev and Rudzkis Testbakshaev_rudzkis
Create various case studiescase.studies
Create various case studies for continuous data without parameter estimationcase.studies.cont
Create various case studies for continuous data in 5 dimensions without parameter estimationcase.studies.cont.D5
Discretize 2D data from case studiescase.studies.disc
Create various case studies with parameter estimationcase.studies.est
Chi-square test for 2D datachi_cont_test
Chi-square test for discrete 2D datachi_disc_test
Power Estimation of Chi Square Testschi_power
Compute M statistic for one dtasetcompute_M_for_dtaset
Bins continuous datadiscretize
Create plot for any case studydraw_case
Estimate E and Var/n at Eval for given h, using MC from rnullestimateEV
examples.mdgof.vignetteexamples.mdgof.vignette
Find gaussian kernel pdfgauss_kernel_matrix
Find evaluation pointsgen_eval
Create copula objectsgen.cop
Power estimation of goodness-of-fit tests.gof_power
Tests for the multivariate goodness-of-fit problemgof_test
Adjusted p valuesgof_test_adjusted_pvalue
Find gradient of log(f) for a matrix of pointsgrad_mat
Power Estimation for the multivariate goodness-of-fit problem via twosample testshybrid_power
Tests for the multivariate goodness-of-fit problem via twosample testshybrid_test
hybrid.mdgof.vignettehybrid.mdgof.vignette
Find test statistic for Kernel Stein Discrepancy testksd
Example for a new testnewTS
Find probabilities from cdf for discrete datap2dC
power_studies_cont_D5_hybrid_resultspower_studies_cont_D5_hybrid_results
power_studies_cont_D5_resultspower_studies_cont_D5_results
power_studies_cont_est_resultspower_studies_cont_est_results
power_studies_cont_hybrid_resultspower_studies_cont_hybrid_results
power_studies_cont_nMC5_D5_hybrid_resultspower_studies_cont_nMC5_D5_hybrid_results
power_studies_cont_nMC5_hybrid_resultspower_studies_cont_nMC5_hybrid_results
power_studies_cont_resultspower_studies_cont_results
power_studies_disc_est_resultspower_studies_disc_est_results
power_studies_disc_hybrid_resultspower_studies_disc_hybrid_results
power_studies_disc_nMC5_hybrid_resultspower_studies_disc_nMC5_hybrid_results
power_studies_disc_resultspower_studies_disc_results
Print an MDgof power result with Monte Carlo uncertaintyprint.MDgof_power
Methods for MDgof goodness-of-fit test resultsas.data.frame.MDgof_test print.MDgof_test print.summary.MDgof_test summary.MDgof_test
Ripley's K function testRipleyK
Benchmarking for Multivariate Goodness-of-fit Testsrun.studies
Rearrange 2D discrete datasq2rec
Find test statistics for continuous dataTS_cont
Find test statistics for discrete dataTS_disc