Changes in version 2.3.5 (2025-04-02) - Explicitly uses current value of mclust.options("hcUse") for initializing the EM iterative estimation process. - Fixed anchors in Rd \link{} targets that were not within the package, addressing CRAN requirements. Changes in version 2.3.4 (2020-12-16) - Bug fixes and polish. Changes in version 2.3.3 (2018-11-19) - Added the final estimated model to the clustvarsel object. - Solved a bug that stop execution in the greedy-backward search when no variables could be removed. Changes in version 2.3.2 (2018-04-09) - Package version accompanying JSS paper. - Bug fixes in the extreme case no clustering variable is selected using the greedy forward/backward search. Changes in version 2.3.1 (2017-07-07) - Fix bug on a if executed with a condition that has length greater than 1. Changes in version 2.3 (2017-02-24) - Add optional argument verbose to clustvarsel() for printing steps info during the search. - New print method for clustvarsel objects. - A parallel cluster is automatically stopped unless a registered parallel back end is provided as argument to parallel argument in the clustvarsel() function call. - Add "A quick tour of clustvarsel" vignette. Changes in version 2.2 (2015-11-19) - Reformat summary output from clustvarsel object. - Add and update references in main help page. Changes in version 2.1 (2014-10-15) - Version associated with JSS paper submission. - Add explicitly stop of clusters if parallel is used. - Specifically included in the hc() function call the argument name data = ... so that works with both mclust version 4.4 and upper. - Other bug fixes and improvements. Changes in version 2.0 (2013-10-25) - Partial rewriting of the package. - "greedy" search has option for forward and backward direction. - "headlong" search has option only for forward direction in this release. - In clustvarsel() argument G is not the maximum number of clusters but it must be a vector of number of cluster to look for. - No separate code for sampling and no-sampling version of each search algorithm. - Inclusion of argument hcModel to control the initial hierarchical clustering. - Include subset selection in the regression of proposed variable on the variables already included. - "greedy" search algorithms can be executed either sequentially or using the parallel computing facilities available in R. - This version of the package requires R (>= 3.0.0) and mclust (>= 4.0). Changes in version 1.3 (2009-08-04) - Last version on CRAN available for R-2.14.x and mclust version 3.5