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''

General Linear Ballistic Accumulator Models
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.

Fits latent class analysis (LCA) including group variable and covariates.
The group variable can be handled either by multilevel LCA described in Vermunt (2003) <DOI:10.1111/j.0081-1750.2003.t01-1-00131.x> or standard LCA at each level of group variable.
The covariates can be incorporated in the form of logistic regression (Bandeen-Roche et al. (1997) <DOI:10.1080/01621459.1997.10473658>).

Latent Class Analysis with Group Variable
c(person("Youngsun", "Kim", email = "kim0sun@korea.ac.kr", role = c("aut", "cre")),
person("Hwan", "Chung", email = "hwanch@korea.ac.kr", role = c("aut")))
Youngsun Kim [aut, cre],
Hwan Chung [aut]

An R Package for Estimating a Group-Effect in Latent Class
Analysis

Estimating a Group-Effect in Latent Class Analysis

An R Package for Multiple-Group Latent Class Analysis
Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al, 1997 <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman, 1985 <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt, 2003 <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).