Packaged: Wed Nov 26 23:15:33 2008; yuriiaulchenko

Packaged: Thu Jan 29 17:47:28 2009; yuriiaulchenko

Depends: R (>= 2.4.0), methods, genetics, haplo.stats, MASS
Packaged: 2009-07-23 00:52:51 UTC; yuriiaulchenko

Packaged: 2009-09-02 23:48:59 UTC; yuriiaulchenko

Suggests: qvalue, genetics, haplo.stats, DatABEL
Packaged: 2010-04-03 22:42:42 UTC; yuriiaulchenko

Packaged: 2010-05-07 11:56:42 UTC; yuriiaulchenko

Packaged: 2010-05-11 14:12:37 UTC; yuriiaulchenko

Packaged: 2010-05-29 18:33:42 UTC; yuriiaulchenko

Packaged: 2010-06-10 23:36:59 UTC; yuriiaulchenko

Packaged: 2010-06-22 00:02:14 UTC; yuriiaulchenko

Suggests: qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0)
Packaged: 2010-08-09 03:14:36 UTC; yuriiaulchenko

Packaged: 2010-08-30 20:59:34 UTC; yuriiaulchenko

Author: Yurii Aulchenko, Maksim Struchalin, et al.
Packaged: 2011-02-07 19:56:05 UTC; yuriiaulchenko

Suggests: qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0), hglm,
quantitative or binary traits and single-nucleotide
Packaged: 2011-05-02 10:39:26 UTC; yuriiaulchenko

Packaged: 2011-05-18 11:08:31 UTC; yuriiaulchenko

Packaged: 2011-08-29 22:36:02 UTC; yuryaulchenko

Packaged: 2011-08-30 09:38:57 UTC; yuryaulchenko

Depends: R (>= 2.10.0), methods, MASS, utils

Contact: GenABEL project developers <genabel.project@gmail.com>
Packaged: 2012-09-17 11:31:38 UTC; yuryaulchenko

Packaged: 2013-01-09 19:43:58 UTC; yuryaulchenko

Packaged: 2013-02-22 21:41:45 UTC; yuryaulchenko

Contact: GenABEL project developers <genabel.project at gmail.com>
Depends: R (>= 2.15.0), methods, MASS, utils, GenABEL.data

Title: Package contains data which is used by GenABEL example and test
Author: Maksim Struchalin <m.v.struchalin@mail.ru> and GenABEL project developers <genabel.project@gmail.com>
Description: GenABEL.data package consists of a data set used by GenABEL functions

Title: Classes and Methods to Use Genetic Algorithms for Feature
Imports: methods, stats, MASS, oompaBase (>= 3.0.1), ClassDiscovery
Description: Defines classes and methods that can be used
to implement genetic algorithms for feature selection.  The idea is
that we want to select a fixed number of features to combine into a
linear classifier that can predict a binary outcome, and can use a
genetic algorithm heuristically to select an optimal set of features.