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: |
0.2.1 |
Depends: |
R (≥ 3.3.0) |
Imports: |
crayon, dplyr (≥ 0.8.0), forcats, ggplot2 (≥ 3.3.0), infotheo, magrittr, MASS, pillar, progress, purrr, Rcpp, rlang, stringr, tibble, tidyr (≥ 1.0.0) |
LinkingTo: |
Rcpp, RcppArmadillo |
Suggests: |
covr, genieclust, ggcorrplot, gtools, knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
Published: |
2024-05-22 |
DOI: |
10.32614/CRAN.package.partition |
Author: |
Joshua Millstein [aut],
Malcolm Barrett
[aut, cre],
Katelyn Queen
[aut] |
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 |
Citation: |
partition citation info |
Materials: |
README NEWS |
CRAN checks: |
partition results |