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 
[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 |
| Citation: |
partition citation info |
| Materials: |
README NEWS |
| CRAN checks: |
partition results |
Documentation:
Downloads:
Reverse dependencies:
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