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.

Version: 0.1.0
Depends: R (≥ 3.3.0)
Imports: Rcpp, rlang, purrr, tibble, dplyr (≥ 0.8.0), tidyr, ggplot2 (≥ 3.0.0), stringr, magrittr, crayon, pillar, infotheo, MASS, forcats
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, spelling, testthat, knitr, rmarkdown
Published: 2019-05-17
Author: Joshua Millstein [aut], Malcolm Barrett ORCID iD [aut, cre]
Maintainer: Malcolm Barrett <malcolmbarrett at gmail.com>
BugReports: https://github.com/USCbiostats/partition/issues
License: MIT + file LICENSE
URL: https://uscbiostats.github.io/partition/, https://github.com/USCbiostats/partition
NeedsCompilation: yes
Language: en-US
Materials: README
CRAN checks: partition results

Downloads:

Reference manual: partition.pdf
Vignettes: Extending partition
Introduction to Partition
Package source: partition_0.1.0.tar.gz
Windows binaries: r-devel: partition_0.1.0.zip, r-devel-gcc8: partition_0.1.0.zip, r-release: partition_0.1.0.zip, r-oldrel: partition_0.1.0.zip
OS X binaries: r-release: partition_0.1.0.tgz, r-oldrel: partition_0.1.0.tgz

Linking:

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