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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

Documentation:

Reference manual: rFSA.pdf

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rFSA to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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