micompr: An R Package for Multivariate Independent Comparison of Observations

TL;DR

The micompr R package provides a distribution-independent method for comparing multivariate samples using PCA and statistical tests, suitable for high-dimensional data like time series and images.

Contribution

It introduces an automated, distribution-free approach for multivariate sample comparison leveraging PCA and statistical testing within an R package.

Findings

Effective in high-dimensional data comparison

Automatically selects features explaining differences

Applicable to diverse data types like images and time series

Abstract

The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. 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. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations.