person(given = "John",
family = "Muschelli",
role = c("aut", "cre"),
email = "muschellij2@gmail.com")
Interacts with the 'Glassdoor' API
<https://www.glassdoor.com/developer/index.htm>. Allows the user to
search job statistics, employer statistics, and job progression,
where 'Glassdoor' provides a breakdown of other jobs a person did
after their current one.

Graphical Lasso: Estimation of Gaussian Graphical Models
Jerome Friedman, Trevor Hastie and Rob Tibshirani
Estimation of a sparse inverse covariance matrix using a lasso (L1)
penalty. Facilities are provided for estimates along a path of values
for the regularization parameter.

Graphical lasso- estimation of Gaussian graphical models

Matyas A Sustik [aut, cph],
Ben Calderhead [aut, cph],
Julien Clavel [com, ctb]
A fast and improved implementation of the graphical LASSO.

High dimensional Sparse Gaussian Graphical Mixture Model
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''

High dimensional Mixture Graph Models selection
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
function ``gm.plot''