Packaged: Thu Jan  8 07:40:34 2009; tibs

Packaged: Thu Feb  5 17:51:45 2009; ripley

Title: Graphical lasso- estimation of Gaussian graphical models

Description: A fast and improved implementation of the graphical LASSO.
Packaged: 2018-07-26 11:48:33 UTC; julienclavel

Title: High dimensional Sparse Gaussian Graphical Mixture Model
Description: The R package glassomix is a statistical tool for
performing model-based soft clustering.  It provides functions
for parameter estimation via the EM algorithm for Gaussian
graphical mixture models in high dimensional setting. The main
function is ``glasso.mix'' upon which a model selection is
performed. The package estimates the optimum number of mixture
components (K) and the tuning parameter (lambda) based on the
Extended Bayesian Information Criteria (EBIC) via ``gm.select''
function.  The graphical structural of the K-networks are also
plotted through the function ``gm.plot''

Title: High dimensional Mixture Graph Models selection
Depends: R (>= 3.0.0), mvtnorm, glasso, huge
Description: The package glassomix provides a general framework for
network recovering through a model-based soft clustering. It
provides functions for parameter estimation via the EM
algorithm for Gaussian graphical mixture models in high
dimensional setting. The main function is ``glasso.mix'' upon
which a model selection is performed. The package estimates the
optimum number of mixture components (K) and the tuning
parameter (lambda) based on the Extended Bayesian Information
Criteria (EBIC) via ``select.gm'' function.  The graphical
structural of the K-networks are also plotted through the

Description: The  package glassomix provides a general
framework  for network recovering through
a model-based soft clustering. It provides
functions for parameter estimation via the
EM algorithm for Gaussian graphical mixture
models in high dimensional setting. The main
function is ``glasso.mix'' upon which a model
selection is performed. The package estimates
the optimum number of mixture components, K
and the tuning parameter, lambda, based on
the Extended Bayesian Information Criteria
The graphical structural of the K-networks
are also plotted through the function ``gm.plot''