tidygraph, rlang, igraph, stats, mgcv, digest, dplyr

Agglomerative Partitioning Framework for Dimension Reduction
c(
person(
"Joshua",
"Millstein",
role = "aut",
email = "joshua.millstein@usc.edu"),
person(
"Malcolm",
"Barrett",
role = c("aut", "cre"),
email = "malcolmbarrett@gmail.com",
comment = c(ORCID = "0000-0003-0299-5825")))
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.
Rcpp, rlang, purrr, tibble, dplyr (>= 0.8.0), tidyr, ggplot2
(>= 3.0.0), stringr, magrittr, crayon, pillar, infotheo, MASS,
forcats
covr, spelling, testthat, knitr, rmarkdown
Joshua Millstein [aut],
Malcolm Barrett [aut, cre] (<https://orcid.org/0000-0003-0299-5825>)

Rcpp, rlang, purrr, tibble, dplyr (>= 0.8.0), tidyr (>=
1.0.0), ggplot2 (>= 3.0.0), stringr, magrittr, crayon, pillar,
infotheo, MASS, forcats

c(person(given = "Joshua",
family = "Millstein",
role = "aut",
email = "joshua.millstein@usc.edu"),
person(given = "Malcolm",
family = "Barrett",
role = c("aut", "cre"),
email = "malcolmbarrett@gmail.com",
comment = c(ORCID = "0000-0003-0299-5825")))
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>.
crayon, dplyr (>= 0.8.0), forcats, ggplot2 (>= 3.3.0),
infotheo, magrittr, MASS, pillar, purrr, Rcpp, rlang, stringr,
tibble, tidyr (>= 1.0.0)
covr, knitr, rmarkdown, spelling, testthat, ggcorrplot