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Summary

The micompr R package implements a procedure for comparing multivariate samples associated with different groups. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations.

How to install

Install the development version from GitHub with the following command (requires the devtools package):

devtools::install_github("nunofachada/micompr")

A stable version of the package is available on CRAN and can be installed with the following instruction:

install.packages("micompr")

Documentation

All methods and functions are fully documented and can be queried using the built-in help system. After installation, to access the man pages, invoke the micompr help page as follows:

help("micompr")

Additionally, the package contains two vignettes with a number of examples.

References

Practice

  • Fachada N, Rodrigues J, Lopes VV, Martins RC, Rosa AC. (2016) micompr: An R

Package for Multivariate Independent Comparison of Observations. The R Journal 8(2):405–420. https://doi.org/10.32614/RJ-2016-055

Theory

  • Fachada N, Lopes VV, Martins RC, Rosa AC. (2017)

Model-independent comparison of simulation output. Simulation Modelling Practice and Theory. 72:131–149. https://doi.org/10.1016/j.simpat.2016.12.013 (arXiv preprint)

License

MIT License

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Version

Install

install.packages('micompr')

Monthly Downloads

304

Version

1.3.0

License

MIT + file LICENSE

Issues

0

Pull Requests

0

Stars

3

Forks

1

Maintainer

Nuno Fachada

Last Published

August 25th, 2025

Functions in micompr (1.3.0)

pphpc_noshuff

Data from two implementations of the PPHPC model, one of which has agent list shuffling deactivated
pphpc_testvlo

Data for testing variable length outputs
pphpc_ok

Data from two similar implementations of the PPHPC model
print.assumptions_cmpoutput

Print method for the assumptions of parametric tests used in a comparison of an output
pst

Concatenate strings without any separator characters
pvalcol

Associate colors to p-values
print.assumptions_manova

Print information about the assumptions of the MANOVA test
summary.micomp

Summary method for multiple comparisons of outputs
summary.grpoutputs

Summary method for grouped outputs
print.assumptions_micomp

Print information about the assumptions concerning the parametric tests performed on multiple comparisons of outputs
pphpc_diff

Data from two implementations of the PPHPC model, one of which setup with a different parameter
toLatex.micomp

Convert micomp object to LaTeX table
print.assumptions_paruv

Print information about the assumptions of the parametric test
print.cmpoutput

Print information about comparison of an output
pvalf

Format p-values
tikzscat

Simple TikZ scatter plot
pvalf.default

Default p-value formatting method
summary.assumptions_micomp

Summary method for the assumptions of parametric tests used in multiple comparisons of outputs
summary.cmpoutput

Summary method for comparison of an output
toLatex.cmpoutput

Convert cmpoutput object to LaTeX table
print.micomp

Print information about multiple comparisons of outputs
pvalnum

Make sure p-values are numeric
print.grpoutputs

Print information about grouped outputs
tscat_apply

Multiple TikZ 2D scatter plots for a list of output comparisons.
summary.assumptions_cmpoutput

Summary method for the assumptions of parametric tests used in a comparison of an output
cmpoutput

Compares output observations from two or more groups
assumptions.micomp

Get assumptions for parametric tests performed on each comparisons
assumptions_manova

Determine the assumptions for the MANOVA test
grpoutputs

Load and group outputs from files
assumptions.cmpoutput

Get assumptions for parametric tests performed on output comparisons
micomp

Multiple independent comparisons of observations
assumptions_paruv

Determine the assumptions for the parametric comparison test
assumptions

Parametric tests assumptions
concat_outputs

Concatenate multiple outputs with multiple observations
centerscale

Center and scale vector
plot.grpoutputs

Plot grouped outputs
plot.micomp

Plot projection of output observations on the first two dimensions of the principal components space
plot.assumptions_micomp

Plot p-values for testing the assumptions of the parametric tests used in multiple output comparison
plot.assumptions_manova

Plot p-values for testing the multivariate normality assumptions of the MANOVA test
plot.assumptions_cmpoutput

Plot p-values for testing the assumptions of the parametric tests used in output comparison
plot.cmpoutput

Plot comparison of an output
plot.assumptions_paruv

Plot p-values for testing the assumptions of the parametric tests used in output comparison
plotcols

Default colors for plots in micomp package
micompr-package

micompr: Multivariate Independent Comparison of Observations