grDevices, graphics, genalg, mvtnorm, bdsmatrix, MASS,
progress, parallel, stats, MSQC, praznik

c(person(given = "Kristian Hovde",
family = "Liland",
role = c("aut", "cre"),
email = "kristian.liland@nmbu.no",
comment = c(ORCID = "0000-0001-6468-9423")),
person(given = "Tahir",
family = "Mehmood",
role = c("ctb")),
person(given = "Solve",
family = "Sæbø",
role = c("ctb")))
Kristian Hovde Liland [aut, cre]
(<https://orcid.org/0000-0001-6468-9423>),
Tahir Mehmood [ctb],
Solve Sæbø [ctb]

Partial Least Squares (PLS) Data Analysis Methods
person("Gaston", "Sanchez", role=c("aut", "cre"), email =
"gaston.stat@gmail.com")
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
Tenenhaus.
'nipals.R' 'plot.nipals.R' 'plot.plsca.R' 'plot.plsreg1.R'
'plot.plsreg2.R' 'plsca.R' 'plsreg1.R' 'plsreg2.R'
'print.nipals.R' 'print.plsca.R' 'print.plsreg1.R'
'print.plsreg2.R' 'interbat.R' 'plot.interbat.R'
'print.interbat.R' 'print.simpls.R' 'print.simplsca.R'
'simpls.R' 'simplsca.R' 'plot.simpls.R' 'plot.simplsca.R'

Degrees of Freedom and Confidence Intervals for Partial Least
Squares Regression
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
intervals and tests.

Degrees of Freedom for Partial Least Squares Regression
The \code{psldof} 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.