partition: 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) <doi:10.1093/bioinformatics/btz661>.

Version: 0.1.4
Depends: R (≥ 3.3.0)
Imports: crayon, dplyr (≥ 0.8.0), forcats, ggplot2 (≥ 3.3.0), infotheo, magrittr, MASS, pillar, purrr, Rcpp, rlang, stringr, tibble, tidyr (≥ 1.0.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, knitr, rmarkdown, spelling, testthat (≥ 3.0.0), ggcorrplot
Published: 2021-10-05
Author: Joshua Millstein [aut], Malcolm Barrett ORCID iD [aut, cre]
Maintainer: Malcolm Barrett <malcolmbarrett at>
License: MIT + file LICENSE
NeedsCompilation: yes
Language: en-US
Citation: partition citation info
Materials: README NEWS
CRAN checks: partition results


Reference manual: partition.pdf
Vignettes: Extending partition
Introduction to Partition


Package source: partition_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): partition_0.1.4.tgz, r-oldrel (arm64): partition_0.1.4.tgz, r-release (x86_64): partition_0.1.4.tgz, r-oldrel (x86_64): partition_0.1.4.tgz
Old sources: partition archive

Reverse dependencies:

Reverse imports: modACDC


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