Package: sNPLS 1.0.40

sNPLS: NPLS Regression with L1 Penalization

Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 <doi:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores.

Authors:David Hervas

sNPLS_1.0.40.tar.gz
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sNPLS_1.0.40.tgz(r-4.4-any)sNPLS_1.0.40.tgz(r-4.3-any)
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sNPLS.pdf |sNPLS.html✨
sNPLS/json (API)

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

Peer review:

Bug tracker:https://github.com/david-hervas/snpls/issues

Datasets:

On CRAN:

7 exports 0.84 score 56 dependencies 2 scripts 260 downloads

Last updated 9 months agofrom:373054a149. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-winNOTEAug 26 2024
R-4.5-linuxNOTEAug 26 2024
R-4.4-winNOTEAug 26 2024
R-4.4-macNOTEAug 26 2024
R-4.3-winNOTEAug 26 2024
R-4.3-macNOTEAug 26 2024

Exports:auroccv_fitcv_snplsga_snplsrepeat_cvsNPLSSR

Dependencies:beeswarmcliclickRcodetoolscolorspacecrayondigestfansifarverFNNforeachfuturefuture.applyGAggplot2ggrepelglobalsgluegtableisobanditeratorskernlabKernSmoothkslabelinglatticelifecyclelistenvmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmeparallellypbapplypillarpkgconfigplyrpracmapROCR6RColorBrewerRcppRcppArmadillorlangscalesstringdisttibbleutf8vctrsviridisLitewithr