Package: pls 2.9-0

pls: Partial Least Squares and Principal Component Regression

Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).

Authors:Kristian Hovde Liland [aut, cre], Bjørn-Helge Mevik [aut], Ron Wehrens [aut], Paul Hiemstra [ctb]

pls_2.9-0.tar.gz
pls_2.9-0.zip(r-4.7-any)pls_2.9-0.zip(r-4.6-any)pls_2.9-0.zip(r-4.5-any)
pls_2.9-0.tgz(r-4.6-any)pls_2.9-0.tgz(r-4.5-any)
pls_2.9-0.tar.gz(r-4.7-any)pls_2.9-0.tar.gz(r-4.6-any)
pls_2.9-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pls/json (API)

# Install 'pls' in R:

install.packages('pls', repos = c('https://khliland.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/khliland/pls/issues

Pkgdown/docs site:https://khliland.github.io

Datasets:

gasoline - Octane numbers and NIR spectra of gasoline
mayonnaise - NIR measurements and oil types of mayonnaise
oliveoil - Sensory and physico-chemical data of olive oils
yarn - NIR spectra and density measurements of PET yarns

On CRAN:

13.75 score 40 stars 97 packages 4.0k scripts 30k downloads 137 mentions 43 exports 0 dependencies

Last updated from:930db7cc9d. Checks:2 NOTE, 7 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	126
source / vignettes	OK	177
linux-release	OK	105
macos-release	OK	113
macos-oldrel	OK	110
windows-devel	NOTE	87
windows-release	OK	80
windows-oldrel	OK	83
wasm-release	OK	125

Exports:coefplot compnames corrplot cppls cppls.fit crossval cvsegments explvar fac2seg jack.test kernelpls.fit loading.weights loadingplot loadings msc MSEP mvr mvrValstats nipals nipals.fit nipalspc.fit nipalspcr oscorespls.fit pcr pls.options plsr prednames predplot predplotXy R2 respnames RMSEP scoreplot scores selectNcomp simpls.fit stdize svdpc.fit validationplot var.jack widekernelpls.fit Yloadings Yscores

Dependencies:

Introduction to the pls Package

Rendered frompls-manual.Rmdusingknitr::rmarkdown

Last update: 2026-02-20
Started: 2026-02-20

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Biplots of PLSR and PCR Models.	biplot.mvr
Extract Information From a Fitted PLSR or PCR Model	coef.mvr compnames explvar fitted.mvr model.frame.mvr model.matrix.mvr prednames residuals.mvr respnames
Plot Regression Coefficients of PLSR and PCR models	coefplot
CPPLS (Indahl et al.)	cppls.fit
Cross-validation of PLSR and PCR models	crossval
Generate segments for cross-validation	cvsegments
Factor to Segments	fac2seg
Octane numbers and NIR spectra of gasoline	gasoline
Jackknife approximate t tests of regression coefficients	jack.test print.jacktest
Kernel PLS (Dayal and MacGregor)	kernelpls.fit
NIR measurements and oil types of mayonnaise	mayonnaise
Multiplicative Scatter Correction	makepredictcall.msc msc predict.msc
Partial Least Squares and Principal Component Regression	cppls mvr nipals nipalspcr pcr plsr
Cross-validation	mvrCv
MSEP, RMSEP and R2 of PLSR and PCR models	MSEP MSEP.mvr mvrVal mvrValstats R2 R2.mvr RMSEP RMSEP.mvr
NIPALS PLS with missing values	nipals.fit
NIPALS PCR with missing values	nipalspc.fit
Sensory and physico-chemical data of olive oils	oliveoil
Orthogonal scores PLSR	oscorespls.fit
Plot Method for MVR objects	plot.mvr
Set or return options for the pls package	pls.options
Predict Method for PLSR and PCR	predict.mvr
Prediction Plots	predplot predplot.default predplot.mvr predplotXy
Summary and Print Methods for PLSR and PCR objects	as.data.frame.mvrVal print.mvr print.mvrVal summary.mvr
Plots of Scores, Loadings and Correlation Loadings	corrplot loadingplot loadingplot.default plot.loadings plot.scores scoreplot scoreplot.default
Extract Scores and Loadings from PLSR and PCR Models	loading.weights loadings loadings.default scores scores.default Yloadings Yscores
Suggestions for the optimal number of components in PCR and PLSR models	selectNcomp
Sijmen de Jong's SIMPLS	simpls.fit
Standardization of Data Matrices	makepredictcall.stdized predict.stdized stdize
Principal Component Regression	svdpc.fit
Validation Plots	plot.mvrVal validationplot
Jackknife Variance Estimates of Regression Coefficients	var.jack
Calculate Variance-Covariance Matrix for a Fitted Model Object	vcov.mvr
Wide Kernel PLS (Rännar et al.)	widekernelpls.fit
NIR spectra and density measurements of PET yarns	yarn