Package: carsAlgo 0.5.0

carsAlgo: Competitive Adaptive Reweighted Sampling (CARS) Algorithm

Implements Competitive Adaptive Reweighted Sampling (CARS) algorithm for variable selection from high-dimensional dataset using Partial Least Squares (PLS) regression models. CARS algorithm iteratively applies the Monte Carlo sub-sampling and exponential variable elimination techniques to identify/select the most informative variables/features subjected to minimal cross-validated RMSE score. The implementation of CARS algorithm is inspired from the work of Li et al. (2009) <doi:10.1016/j.aca.2009.06.046>. This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), hyperspectral chemometrics areas, etc.

Authors:Md. Ashraful Haque [aut, cre], Avijit Ghosh [aut], Sayantani Karmakar [aut], Harsh Sachan [aut], Shalini Kumari [aut]

carsAlgo_0.5.0.tar.gz
carsAlgo_0.5.0.zip(r-4.7)carsAlgo_0.5.0.zip(r-4.6)carsAlgo_0.5.0.zip(r-4.5)
carsAlgo_0.5.0.tgz(r-4.6-any)carsAlgo_0.5.0.tgz(r-4.5-any)
carsAlgo_0.5.0.tar.gz(r-4.7-any)carsAlgo_0.5.0.tar.gz(r-4.6-any)
carsAlgo_0.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
carsAlgo/json (API)
NEWS

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

Bug tracker:https://github.com/mah-iasri/carsalgo/issues

On CRAN:

Conda:

3.18 score 1 stars 1 scripts 526 downloads 2 exports 18 dependencies

Last updated from:444f486379. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK152
source / vignettesOK164
linux-release-x86_64OK109
macos-release-arm64OK169
macos-oldrel-arm64OK218
windows-develOK71
windows-releaseOK68
windows-oldrelOK66
wasm-releaseOK94

Exports:CARSAlgorithmfit

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleplsR6RColorBrewerrlangS7scalesvctrsviridisLitewithr