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-4.5-noble)RandomForestsGLS_0.1.5.tar.gz(r-4.4-noble)
RandomForestsGLS_0.1.5.tgz(r-4.4-emscripten)RandomForestsGLS_0.1.5.tgz(r-4.3-emscripten)
RandomForestsGLS.pdf |RandomForestsGLS.html✨
RandomForestsGLS/json (API)

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

Peer review:

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

openblascppopenmp

2.00 score 8 scripts 330 downloads 4 exports 8 dependencies

Last updated 3 months agofrom:f6250910b5. Checks:OK: 1 WARNING: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linux-x86_64WARNINGDec 02 2024

Exports:RFGLS_estimate_spatialRFGLS_estimate_timeseriesRFGLS_predictRFGLS_predict_spatial

Dependencies:BRISCmatrixStatspbapplyrandomForestRANNRcppRcppArmadillordist

How to use RandomForestsGLS

Rendered fromRandomForestsGLS_user_guide.Rmdusingknitr::rmarkdownon Dec 02 2024.

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