'PCAmix.R' 'PCArot.R' 'plot.PCAmix.R' 'predict.PCAmix.R'
'predict.MFAmix.R' 'print.PCAmix.R' 'recod.R' 'recodqual.R'
'recodquant.R' 'sol.2dim.R' 'summary.PCAmix.R'
'tab.disjonctif.NA.R' 'svd.triplet.R' 'Cut.Group.R'
'nb.level.R' 'Tri.Data.R' 'Lg.pond.R' 'Lg.R' 'RV.R' 'RV.pond.R'
'MFAmix.R' 'plot.MFAmix.R' 'print.MFAmix.R' 'summary.MFAmix.R'

Marie Chavent and Vanessa Kuentz and Amaury Labenne and Benoit Liquet and Jerome Saracco.
Principal Component Analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables.

c(person("Marie", "Chavent", role = c("aut", "cre"),
email = "Marie.Chavent@u-bordeaux.fr"),
person("Vanessa", "Kuentz", role = "aut"),
person("Amaury", "Labenne", role = "aut"),
person("Benoit", "Liquet", role = "aut"),
person("Jerome", "Saracco", role = "aut"))
Marie Chavent [aut, cre],
Vanessa Kuentz [aut],
Amaury Labenne [aut],
Benoit Liquet [aut],
Jerome Saracco [aut]
Principal Component Analysis, orthogonal rotation and multiple
factor analysis for a mixture of quantitative and qualitative variables.

Implements principal component analysis, orthogonal rotation and multiple
factor analysis for a mixture of quantitative and qualitative variables.

Semiparametric regression analysis of panel count data using
monotone splines
Bin Yao <yaob@email.sc.edu> and Lianming Wang <wangl@stat.sc.edu>
Semiparametric regression analysis of panel count data under the non-homogeneous Poisson
process model with and without Gamma frailty using monotone splines.

Finding the Number of Significant Principal Components
Implements methods to automate the Auer-Gervini graphical
Bayesian approach for determining the number of significant
principal components. Automation uses clustering, change points, or
simple statistical models to distinguish "long" from "short" steps
in a graph showing the posterior number of components as a function
of a prior parameter. See <doi:10.1101/237883>.
methods, stats, graphics, oompaBase, kernlab, changepoint, cpm