rFSA: Feasible Solution Algorithm for Finding Best Subsets and Interactions

Assists in statistical model building to find optimal and semi-optimal higher order interactions and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution algorithm comes up with model forms of a specific type that can have fixed variables, higher order interactions and their lower order terms.

Version: 0.9.6
Imports: parallel, methods, tibble, rPref, tidyr, hash
Published: 2020-06-10
DOI: 10.32614/CRAN.package.rFSA
Author: Joshua Lambert [aut, cre], Liyu Gong [aut], Corrine Elliott [aut], Sarah Janse [ctb]
Maintainer: Joshua Lambert <joshua.lambert at uc.edu>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: rFSA results


Reference manual: rFSA.pdf


Package source: rFSA_0.9.6.tar.gz
Windows binaries: r-devel: rFSA_0.9.6.zip, r-release: rFSA_0.9.6.zip, r-oldrel: rFSA_0.9.6.zip
macOS binaries: r-release (arm64): rFSA_0.9.6.tgz, r-oldrel (arm64): rFSA_0.9.6.tgz, r-release (x86_64): rFSA_0.9.6.tgz, r-oldrel (x86_64): rFSA_0.9.6.tgz
Old sources: rFSA archive


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