Agglomerative Partitioning Framework for Dimension Reduction

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) .


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0.1.4 by Malcolm Barrett, 22 days ago,

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Authors: Joshua Millstein [aut] , Malcolm Barrett [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports crayon, dplyr, forcats, ggplot2, infotheo, magrittr, MASS, pillar, purrr, Rcpp, rlang, stringr, tibble, tidyr

Suggests covr, knitr, rmarkdown, spelling, testthat, ggcorrplot

Linking to Rcpp, RcppArmadillo

See at CRAN