Specification and estimation of conditional logit models of binary
responses and multinomial counts is provided, with or without
random effects. The current implementation of the estimator for random
effects variances uses a Laplace approximation (or PQL) approach and thus should
be used only if groups sizes are large.

Multinomial Logit Models, with or without Random Effects or
Overdispersion
Provides estimators for multinomial logit models in their
conditional logit and baseline logit variants, with or without random effects,
with or without overdispersion.
Random effects models are estimated using the PQL technique (based on a Laplace approximation)
or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated
with caution if the group sizes are small.

C. Fraley and A.E. Raftery, Dept. of Statistics,
University of Washington
R port by Ron Wehrens <rwehrens@sci.kun.nl>
copyright 1996, 1998
Department of Statistics, University of Washington
Permission granted for unlimited redistribution for
non-commercial use only.

Port of the 2002 MCLUST version by Fraley and Raftery
copyright 1996, 1998, 2002 Department of Statistics,
University of Washington
funded by ONR contracts N00014-96-1-0192 and N00014-96-1-0330
and NIH Grant 1 R01 CA94212-01.
Permission granted for unlimited redistribution for
non-commercial use only. Commerical use requires a licensing
agreement with the University of Washington.

C. Fraley and A.E. Raftery, Dept. of Statistics,
University of Washington.
R port by Ron Wehrens <rwehrens@sci.kun.nl>
Model-based cluster analysis: the 2002 version of MCLUST

C. Fraley and A.E. Raftery, Dept. of Statistics,
University of Washington.
R port by Ron Wehrens.

Model-Based Clustering / Normal Mixture Modeling
Model-based clustering and normal mixture modeling
including Bayesian regularization

Chris Fraley, Adrian Raftery and Luca Scrucca
Normal Mixture Modeling for Model-Based Clustering,
Classification, and Density Estimation
Normal Mixture Modeling fitted via EM algorithm for
Model-Based Clustering, Classification, and Density Estimation,
including Bayesian regularization.