Description: Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.

Author: Paul D. McNicholas [aut, cre], Aisha ElSherbiny [aut], K. Raju Jampani [ctb], Aaron F. McDaid [aut], T. Brendan Murphy [aut], Larry Banks [ctb]

Title: The p-generalized normal distribution
Description: Evaluation of the pdf and the cdf of the univariate
p-generalized normal distribution. Sampling from the
p-generalized normal distribution using either the
p-generalized polar method, the p-generalized rejecting polar
method, the Monty Python method, the Ziggurat method or the
method of Nardon and Pianca. The package also includes routines
for the simulation of the bivariate, p-generalized uniform
distribution on the p-generalized unit circle and the
simulation of the corresponding angular distribution.

Title: The p-Generalized Normal Distribution
Description: Evaluation of the pdf and the cdf of the univariate,
noncentral, p-generalized normal distribution. Sampling from
the univariate, noncentral, p-generalized normal distribution
using either the p-generalized polar method, the p-generalized
rejecting polar method, the Monty Python method, the Ziggurat
method or the method of Nardon and Pianca. The package also
includes routines for the simulation of the bivariate,
p-generalized uniform distribution and the simulation of the

Title: Build Dependency Graphs using Projection
Author: Jianqing Fan, Yang Feng and Lucy Xia
Imports: SAM, energy, glasso, glmnet, splines
Description: Implements a general framework for creating graphs using projection. Both lasso and sparse additive model projections and implemented. Both Pearson correlation and distance covariance are used to generate the graph.

Description: Implements a general framework for creating dependency graphs using projection. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance are used to generate the graph.