nlme, sp, spdep, spatialEco, raster, velox, rgeos

exactextractr, methods, nlme, sp, spdep, raster, rgeos, sf

R (>= 2.3.1), methods, genetics, haplo.stats
a package for genome-wide association analysis
between quantitative or binary traits and SNPs

Yurii Aulchenko, Stephan Ripke, Toby Johnson
R (>= 2.4.0), methods, genetics, haplo.stats, qvalue, MASS

Yurii Aulchenko, with contributions from
Stephan Ripke and Toby Johnson

Yurii Aulchenko, with contributions from
Maksim Struchalin, Stephan Ripke and Toby Johnson
a package for genome-wide association analysis between
quantitative or binary traits and single-nucleiotide
polymorphisms (SNPs).

Yurii Aulchenko, Maksim Struchalin, with contributions from
Stephan Ripke and Toby Johnson

Wed Nov 26 23:15:33 2008; yuriiaulchenko

Thu Jan 29 17:47:28 2009; yuriiaulchenko

R (>= 2.4.0), methods, genetics, haplo.stats, MASS

qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0)

Yurii Aulchenko, Maksim Struchalin, et al.

qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0), hglm,
MetABEL, PredictABEL
a package for genome-wide association analysis between
quantitative or binary traits and single-nucleotide
polymorphisms (SNPs).

qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0), hglm,
MetABEL, PredictABEL, VariABEL

qvalue, genetics, haplo.stats, DatABEL (>= 0.9-0), hglm,
MetABEL, PredictABEL, VariABEL, bigRR

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

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

Classes and Methods to Use Genetic Algorithms for Feature
Selection
methods, stats, MASS, oompaBase (>= 3.0.1), ClassDiscovery
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.