Imports: mvtnorm, boot, bipartite, car, MASS
Suggests: plsdof, R.rsp, chemometrics, plsdepot

Description: Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Title: Variable Selection in Partial Least Squares
Author: Kristian Hovde Liland, Tahir Mehmood, Solve Sæbø
Description: Interfaces and methods for variable selection in Partial Least
Squares. The methods include filter methods, wrapper methods and embedded
Imports: grDevices, graphics, genalg, mvtnorm, bdsmatrix, pls, MASS,
Packaged: 2016-03-30 18:39:43 UTC; kristian.liland

Imports: grDevices, graphics, genalg, mvtnorm, bdsmatrix, MASS,
Packaged: 2016-06-15 19:37:47 UTC; kristian.liland

methods. Both regression and classification is supported.
Packaged: 2016-10-12 11:40:35 UTC; kristian.liland

progress, parallel, stats, MSQC, praznik

Packaged: 2016-07-27 18:25:11 UTC; kristian.liland

Title: Partial Least Squares (PLS) Data Analysis Methods
Authors@R: person("Gaston", "Sanchez", role=c("aut", "cre"), email =
Description: plsdepot contains different methods for PLS analysis of
one or two data tables such as Tucker's Inter-Battery, NIPALS,
SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
The main reference for this software is the awesome book (in
French) 'La Regression PLS: Theorie et Pratique' by Michel
Collate: 'nipals.R' 'plot.nipals.R' 'plot.plsca.R' 'plot.plsreg1.R'

Title: Degrees of Freedom and Confidence Intervals for Partial Least
Description: The plsdof package provides Degrees of Freedom estimates
for Kernel Partial Least Squares Regression and its kernel
extension. The package also provides model selection based on
various information criteria (aic, bic, gmdl) and based on
cross-validation. For linear Partial Least Squares, the package
computes the approximate distribution of the regression
coefficients. This allows the construction of confidence