Package: FWDselect 2.1.1

Marta Sestelo

FWDselect: Selecting Variables in Regression Models

A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). Implemented methodology described in: M. Sestelo, N. M. Villanueva, L. Meira-Machado and J. Roca-Pardiñas (2016). FWDselect: an R package for variable selection in regression models. The R Journal, 8 (1), 132-148. <doi:10.32614/RJ-2016-009>.

Authors:Marta Sestelo [aut, cre, cph], Nora M. Villanueva [aut, cph], Javier Roca-Pardinas [aut]

FWDselect_2.1.1.tar.gz
FWDselect_2.1.1.zip(r-4.7-any)FWDselect_2.1.1.zip(r-4.6-any)FWDselect_2.1.1.zip(r-4.5-any)
FWDselect_2.1.1.tgz(r-4.6-any)FWDselect_2.1.1.tgz(r-4.5-any)
FWDselect_2.1.1.tar.gz(r-4.7-any)FWDselect_2.1.1.tar.gz(r-4.6-any)
FWDselect_2.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
FWDselect/json (API)

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

Bug tracker:https://github.com/sestelo/fwdselect/issues

Datasets:
  • diabetes - Diabetes data.
  • episode - Episode of SO2. Pollution incident data.
  • pollution - Emission of SO2. Pollution incident data.

On CRAN:

Conda:

feature-engineeringfeature-selectionmachine-learning-algorithmsnonparametricregresssionvariable-importancevariable-selection

3.19 score 2 stars 39 scripts 312 downloads 1 mentions 3 exports 7 dependencies

Last updated from:fda3cc290a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK126
source / vignettesOK206
linux-release-x86_64OK134
macos-release-arm64OK108
macos-oldrel-arm64OK108
windows-develOK84
windows-releaseOK95
windows-oldrelOK86
wasm-releaseOK122

Exports:qselectionselectiontest

Dependencies:cvToolsDEoptimRlatticeMatrixmgcvnlmerobustbase