Packaged: Fri Sep 26 08:17:39 2008; alexis

Packaged: Tue Oct 14 21:45:41 2008; Alexis

Description: paran is an implementation of Horn's technique for
numerically and graphically evaluating the components or
factors retained in a principle components analysis (PCA) or
common factor analysis (FA). Horn's method contrasts
eigenvalues produced through a PCA or FA on a number of random
data sets of uncorrelated variables with the same number of
variables and observations as the experimental or observational
data set to produce eigenvalues for components or factors that
are adjusted for the sample error-induced inflation. Components
with adjusted eigenvalues greater than one are retained. paran
may also be used to conduct parallel analysis following
Glorfeld's (1995) suggestions to reduce the likelihood of

Description: An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention.

Description: Distributed gradient boosting based on the mboost package. The
parboost package is designed to scale up component-wise functional
gradient boosting in a distributed memory environment by splitting the
observations into disjoint subsets, or alternatively using bootstrap
samples (bagging). Each cluster node then fits a boosting model to its
subset of the data. These boosting models are combined in an ensemble,
either with equal weights, or by fitting a (penalized) regression
model on the predictions of the individual models on the complete
Depends: R (>= 3.0.1), parallel, mboost, party, iterators
Collate: 'crossvalidation.R' 'methods.R' 'parboost.R' 'postprocess.R'