r-cran-huge - 1.3.5-1+b1 main

Provides a general framework for high-dimensional undirected graph
estimation. It integrates data preprocessing, neighborhood screening,
graph estimation, and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is applied to
help relax the normality assumption. In the graph estimation stage, the
graph structure is estimated by Meinshausen-Buhlmann graph estimation or
the graphical lasso, and both methods can be further accelerated by the
lossy screening rule preselecting the neighborhood of each variable by
correlation thresholding.