micompr-package: micompr: Multivariate Independent Comparison of Observations
In fakenmc/micompr: Multivariate Independent Comparison of Observations
micompr-package R Documentation
micompr: Multivariate Independent Comparison of Observations
Description
A procedure for comparing multivariate samples associated with different groups. It 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. The procedure 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. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2016-055")}.
Author(s)
Maintainer: Nuno Fachada faken@fakenmc.com (ORCID)
See Also
Useful links:
fakenmc/micompr documentation built on Feb. 3, 2024, 1:30 a.m.
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| micompr-package | R Documentation |
micompr: Multivariate Independent Comparison of Observations
Description
A procedure for comparing multivariate samples associated with different groups. It 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. The procedure 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. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2016-055")}.
Author(s)
Maintainer: Nuno Fachada faken@fakenmc.com (ORCID)
See Also
Useful links:
R Package Documentation
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