The plsdof package provides Degrees of Freedom estimates
and approximate confidence intervals for Partial Least Squares
Regression. The package also allows model selection based on
various information criteria (aic, bic, gmdl) and based on
cross-validation.

Degrees of Freedom and Statistical Inference for Partial Least
Squares Regression
The plsdof package provides Degrees of Freedom estimates
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
Regression are available.

The plsdof package provides Degrees of Freedom estimates
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 (Kramer and Sugiyama
2012 <doi:10.1198/jasa.2011.tm10107>).
Further, cross-validation procedures for Ridge Regression and
Principal Components Regression are available.

Anne-Laure Boulesteix <boulesteix@stat.uni-muenchen.de>, and
Korbinian Strimmer <korbinian.strimmer@lmu.de>.
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
ChIP-chip analysis.

Anne-Laure Boulesteix <boulesteix@stat.uni-muenchen.de>,
Sophie Lambert-Lacroix <Sophie.Lambert@imag.fr>,
Julie Peyre <Julie.Peyre@imag.fr>, and
Korbinian Strimmer <korbinian.strimmer@lmu.de>.
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 version includes two new classification
methods for microarray data: GSIM and Ridge PLS.