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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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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.