micompr: An R Package for Multivariate Independent Comparison of Observations

03/22/2016
by   Nuno Fachada, et al.
0

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.

READ FULL TEXT

page 10

page 12

research
09/30/2015

Model-independent comparison of simulation output

Computational models of complex systems are usually elaborate and sensit...
research
03/09/2020

Principal Moment Analysis

Principal Moment Analysis is a method designed for dimension reduction, ...
research
06/04/2018

groupICA: Independent component analysis for grouped data

We introduce groupICA, a novel independent component analysis (ICA) algo...
research
01/24/2018

Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM

The numerical availability of statistical inference methods for a modern...
research
07/18/2023

Continuous-time multivariate analysis

The starting point for much of multivariate analysis (MVA) is an n× p da...
research
07/12/2022

Wasserstein multivariate auto-regressive models for modeling distributional time series and its application in graph learning

We propose a new auto-regressive model for the statistical analysis of m...
research
06/19/2021

Fasano-Franceschini Test: an Implementation of a 2-Dimensional Kolmogorov-Smirnov test in R

The univariate Kolmogorov-Smirnov (KS) test is a non-parametric statisti...

Please sign up or login with your details

Forgot password? Click here to reset