Package: RandomForestsGLS 0.1.5

Arkajyoti Saha

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 (2021) <doi:10.1080/01621459.2021.1950003>.

Authors:Arkajyoti Saha [aut, cre], Sumanta Basu [aut], Abhirup Datta [aut]

RandomForestsGLS_0.1.5.tar.gz
RandomForestsGLS_0.1.5.tar.gz(r-4.7-arm64)RandomForestsGLS_0.1.5.tar.gz(r-4.7-x86_64)RandomForestsGLS_0.1.5.tar.gz(r-4.6-arm64)RandomForestsGLS_0.1.5.tar.gz(r-4.6-x86_64)
RandomForestsGLS_0.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RandomForestsGLS/json (API)

# Install 'RandomForestsGLS' in R:
install.packages('RandomForestsGLS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/arkajyotisaha/randomforestsgls/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

2.23 score 17 scripts 336 downloads 4 exports 8 dependencies

Last updated from:f6250910b5. Checks:4 WARNING, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING170
linux-devel-x86_64WARNING155
source / vignettesOK214
linux-release-arm64WARNING163
linux-release-x86_64WARNING139
wasm-releaseOK107

Exports:RFGLS_estimate_spatialRFGLS_estimate_timeseriesRFGLS_predictRFGLS_predict_spatial

Dependencies:BRISCmatrixStatspbapplyrandomForestRANNRcppRcppArmadillordist

How to use RandomForestsGLS

Rendered fromRandomForestsGLS_user_guide.Rmdusingknitr::rmarkdownon Jun 18 2026.

Last update: 2021-01-08
Started: 2021-01-08