Title: Degrees of Freedom for Partial Least Squares Regression
Description: 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

and approximate confidence intervals for Partial Least Squares
Regression. The package also allows model selection based on

Title: Degrees of Freedom and Statistical Inference for Partial Least
for Partial Least Squares (PLS) Regression. Model selection for
PLS is based on various information criteria (aic, bic, gmdl)
or on cross-validation. Estimates for the mean and covariance
of the PLS regression coefficients are available. They allow
the construction of approximate confidence intervals and the
application of test procedures. Further, cross-validation
procedures for Ridge Regression and Principal Components

application of test procedures (Kramer and Sugiyama
Further, cross-validation procedures for Ridge Regression and
Principal Components Regression are available.

Author: Anne-Laure Boulesteix <boulesteix@stat.uni-muenchen.de>, and
Description: This package provides routines for PLS-based genomic analyses.
Currently, it implements PLS methods for classification with microarray
data and for predicting transcription factor activities from combined
Packaged: Mon Apr 11 15:08:26 2005; socher

Packaged: Fri Dec 16 12:23:02 2005; socher

Packaged: Wed Dec 21 14:17:15 2005; socher

Packaged: Tue May 10 12:17:04 2005; socher

It implements PLS methods for classification with microarray
data  and  prediction of transcription factor activities from combined
ChIP-chip analysis. The 1.2 version includes two new classification
methods for microarray data: GSIM and Ridge PLS.
Packaged: Thu Sep 28 10:10:39 2006; socher

ChIP-chip analysis. The >=1.2-1 versions include two new classification
Packaged: Mon Feb 12 13:11:21 2007; boulesteix

Sophie Lambert-Lacroix <Sophie.Lambert@imag.fr>, Julie Peyre
Description: This package provides routines for PLS-based genomic
analyses. It implements PLS methods for classification with
microarray data and prediction of transcription factor
activities from combined ChIP-chip analysis. The >=1.2-1
versions include two new classification methods for microarray