A new version (0.7.1) of package 'subselect' has been uploaded to CRAN. Package 'subselect' provides functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. As of version 0.7 a new function 'leaps' has been added. 'Leaps' performs a branch and bound search for the best variable subsets, according to a specified criterion. 'Leaps' implements Duarte Silva's adaptation (Reference 3) of Furnival and Wilson's Leaps and Bounds Algorithm for variable selection in Regression Analysis. It is viable in identifying optimal subsets for data sets with a moderate number (up to about 30-35) of variables, and very fast for small data sets (up to about 20-25 variables). In package subselect, the quality of given k-subsets of variables are assessed under three criteria (Reference 2). Three additional functions, 'anneal', 'genetic' and 'improve', search for optimal k-variable subsets under those criteria, using three different algorithms: a simulated annealing algorithm, a genetic algorithm and a restricted local improvement algorithm (Reference 1). Among the options, the user can control number of iterations, initial temperature, cooling factors and cooling frequency in simulated annealing, and number of generations, population size, admissibility of clones and presence and frequency of mutations in the genetic algorithm. For all algorithms, it is possible to specify the number of solutions required in one or more cardinalities and to force the solutions to include and/or to exclude given subsets of variables. Here is the DESCRIPTION file for the package: Package: subselect Version: 0.7.1 Date: 2004/03/10 Title: Selecting variable subsets. Author: Jorge Orestes Cerdeira <orestes at isa.utl.pt> Pedro Duarte Silva <psilva at porto.ucp.pt> Jorge Cadima <jcadima at isa.utl.pt> Manuel Minhoto <minhoto at uevora.pt> Maintainer: Jorge Cadima <jcadima at isa.utl.pt> Description: A collection of functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. License: GPL There is a CHANGELOG file in subdirectory 'inst' documenting changes since Version 0.1. BIBLIOGRAPHY: 1) Cadima, J., Cerdeira, J. Orestes and Minhoto, M. (2004) Computational aspects of algorithms for variable selection in the context of principal components. To appear in _Computational Statistics & Data Analysis_ (Special Issue on Applications of Optimization Heuristics to Estimation and Modelling Problems). 2) Cadima, J. and Jolliffe, I.T. (2001). Variable Selection and the Interpretation of Principal Subspaces, _Journal of Agricultural, Biological and Environmental Statistics_, Vol. 6, 62-79. 3) Duarte Silva, A.P. (2002) Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons, _Computational Statistics_, Vol. 17, 251-271. _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/r-packages