RandomForestsGLS: Random Forests for Dependent Data

Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2020) <doi:10.48550/arXiv.2007.15421>.

Version: 0.1.4
Depends: R (≥ 3.3.0)
Imports: BRISC, parallel, stats, matrixStats, randomForest, pbapply
Suggests: knitr, rmarkdown, ggplot2, testthat (≥ 2.1.0)
Published: 2022-04-28
Author: Arkajyoti Saha [aut, cre], Sumanta Basu [aut], Abhirup Datta [aut]
Maintainer: Arkajyoti Saha <arkajyotisaha93 at gmail.com>
BugReports: https://github.com/ArkajyotiSaha/RandomForestsGLS/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/ArkajyotiSaha/RandomForestsGLS
NeedsCompilation: yes
CRAN checks: RandomForestsGLS results


Reference manual: RandomForestsGLS.pdf
Vignettes: How to use RandomForestsGLS


Package source: RandomForestsGLS_0.1.4.tar.gz
Windows binaries: r-devel: RandomForestsGLS_0.1.4.zip, r-release: RandomForestsGLS_0.1.4.zip, r-oldrel: RandomForestsGLS_0.1.4.zip
macOS binaries: r-release (arm64): RandomForestsGLS_0.1.4.tgz, r-oldrel (arm64): RandomForestsGLS_0.1.4.tgz, r-release (x86_64): RandomForestsGLS_0.1.4.tgz
Old sources: RandomForestsGLS archive


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