Description: A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables  in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting MCLUST models. By default the algorithm uses a sequential search, but parallelization is also available.

Description: A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables  in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting MCLUST models. By default the algorithm uses a sequential search, but parallelisation is also available.
Depends: R (>= 3.2), mclust (>= 5.1), BMA (>= 3.18), foreach, iterators

Title: Variable Selection for Gaussian Model-Based Clustering
Description: Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Authors@R: c(person("Nema", "Dean", role = "aut"),
person("Adrian E.", "Raftery", role = "aut"),
Depends: R (>= 3.2), mclust (>= 5.3), BMA (>= 3.18), foreach, iterators
Suggests: MASS, parallel, doParallel, knitr (>= 1.12), rmarkdown (>=

Authors@R: c(person("Nema", "Dean", role = "aut",
Imports: stats, Matrix, BMA (>= 3.18), foreach, iterators

Description: An R package implementing variable selection methodology for Gaussian model-based clustering which allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting MCLUST models. By default the algorithm uses a sequential search, but parallelisation is also available.
Depends: R (>= 3.2), mclust (>= 5.2), BMA (>= 3.18), foreach, iterators