Package: RTFBoost 0.1.0

Xiaomeng Ju

RTFBoost: Robust Tree-Based Boosting for Functional Regression

Implements a family of robust tree-based boosting methods for functional regression, including procedures based on least absolute deviation, M-estimation, and MM-estimation. The package also includes a non-robust tree-based boosting method introduced in Ju X and Salibian-Barrera M. (2024) <doi:10.1007/s00180-023-01364-2>. Functions are provided for model fitting and prediction in regression settings with functional predictors.

Authors:Xiaomeng Ju [aut, cre], Matias Salibian-Barrera [aut]

RTFBoost_0.1.0.tar.gz
RTFBoost_0.1.0.zip(r-4.7-any)RTFBoost_0.1.0.zip(r-4.6-any)
RTFBoost_0.1.0.tar.gz(r-4.7-any)RTFBoost_0.1.0.tar.gz(r-4.6-any)
RTFBoost_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RTFBoost/json (API)

# Install 'RTFBoost' in R:
install.packages('RTFBoost', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • fruitfly - Fruit fly data
  • GED - German electricity prices and demand

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 2 scripts 7 exports 21 dependencies

Last updated from:8a30615127. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK124
source / vignettesOK160
linux-releaseOK127
windows-develOK96
windows-releaseOK96
wasm-releaseOK95

Exports:RTFBoostRTFBoost.controlRTFBoost.predictRTFBoost.validationTREETREE.controlTREE.predict

Dependencies:bootDEoptimRlatticelme4MASSMatrixminqamvtnormnlmenloptrpcaPPpyinitrbibutilsRcppRcppEigenRdpackreformulasRobStatTMrobustbaserpartrrcov