Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

Modelling Multivariate Binary Data with Blocks of Specific
One-Factor Distribution
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.

Stepwise Variable Selection Procedures for Regression Analysis
International-Harvard Statistical Consulting Company <nhsc2010@hotmail.com>
The stepwise variable selection procedure (with iterations
between the 'forward' and 'backward' steps) can be used to obtain
the best candidate final regression model in regression analysis.
All the relevant covariates are put on the 'variable list' to be
selected. The significance levels for entry (SLE) and for stay
(SLS) are usually set to 0.15 (or larger) for being conservative.
Then, with the aid of substantive knowledge, the best candidate
final regression model is identified manually by dropping the
covariates with p value > 0.05 one at a time until all regression
coefficients are significantly different from 0 at the chosen alpha
level of 0.05.