Giulio Costantini, Nils Kappelmann, Sacha Epskamp
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation.
parallel, igraph, qgraph, dplyr, ggplot2, stats

Maximum Likelihood Estimation for Probability Functions from
Data Sets
person("Jaime", "Mosquera", email = "jmosquerag@unal.edu.co", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-1684-4756"))
R (>= 3.3.0), stats, DEoptim, boot, numDeriv, BBmisc
A routine for parameter estimation for any probability density or
mass function implemented in R via maximum likelihood (ML) given a data set. This
routine is a wrapper function specifically developed for ML estimation. There are
included optimization procedures such as 'nlminb' and 'optim' from base package,
and 'DEoptim' Mullen (2011) <doi: 10.18637/jss.v040.i06>. Standard errors are
estimated with 'numDeriv' Gilbert (2011) <http://CRAN.R-project.org/package=numDeriv>.
Jaime Mosquera [aut, cre] (<https://orcid.org/0000-0002-1684-4756>)

c(
person("Jaime", "Mosquera", email = "jmosquerag@unal.edu.co", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-1684-4756")),
person("Freddy", "Hernandez", , "fhernanb@unal.edu.co", role = c("aut"),
comment = c(ORCID = "0000-0001-7459-3329"))
)
A routine for parameter estimation for any probability density or
mass function implemented in R via maximum likelihood (ML) given a data set. This
routine is a wrapper function specifically developed for ML estimation. There are
included optimization procedures such as 'nlminb' and 'optim' from base package,
and 'DEoptim' Mullen (2011) <doi: 10.18637/jss.v040.i06>. Standard errors are
estimated with 'numDeriv' Gilbert (2011) <http://CRAN.R-project.org/package=numDeriv>
or the option 'Hessian = TRUE' of 'optim' function.
Jaime Mosquera [aut, cre] (<https://orcid.org/0000-0002-1684-4756>),
Freddy Hernandez [aut] (<https://orcid.org/0000-0001-7459-3329>)