Movatterモバイル変換

literanger: Fast Serializable Random Forests Based on 'ranger'

An updated implementation of R package 'ranger' by Wright et al, (2017) <doi:10.18637/jss.v077.i01> for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in 'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007) <doi:10.1177/0962280206074463>. Ensembles of classification and regression trees are currently supported. Sparse data of class 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) <doi:10.1016/j.csda.2013.10.025>. Trained forests can be written to and read from storage. Survival and probability forests are not supported in the update, nor is data of class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package for these analyses.

Version:	0.2.0
Depends:	R (≥ 3.6.0)
Imports:	stats
LinkingTo:	cpp11 (≥ 0.4.7),Rcereal (≥ 1.3.2)
Suggests:	Matrix (≥ 1.5.3),testthat (≥ 3.0.0),tibble (≥ 3.2.1)
Published:	2025-07-13
DOI:	10.32614/CRAN.package.literanger
Author:	Stephen Wade [aut, cre], Marvin N Wright [ctb]
Maintainer:	Stephen Wade <stephematician at gmail.com>
BugReports:	https://gitlab.com/stephematician/literanger/-/issues
License:	GPL-3
URL:	https://gitlab.com/stephematician/literanger
NeedsCompilation:	yes
Materials:	README,NEWS
CRAN checks:	literanger results

Documentation:

Downloads:

Reverse dependencies:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=literangerto link to this page.