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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("fakenmc/micompr")

The package has been submitted to CRAN and, if accepted, will be installable via 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. Under peer-review. (arXiv version available at http://arxiv.org/abs/1603.06907)

Theory

  • Fachada N, Lopes VV, Martins RC, Rosa AC. (2016) Model-independent

comparison of simulation output. Under peer-review. (arXiv version available at http://arxiv.org/abs/1509.09174)

License

MIT License

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Install

install.packages('micompr')

Monthly Downloads

225

Version

1.0.0

License

MIT + file LICENSE

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Last Published

May 8th, 2016

Functions in micompr (1.0.0)