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Perform model selection using distribution and probability-based methods, including standardized AIC, BIC, and AICc. These standardized information criteria allow one to perform model selection in a way similar to the prevalent "Rule of 2" method, but formalize the method to rely on probability theory. A novel goodness-of-fit procedure for assessing linear regression models is also available. This test relies on theoretical properties of the estimated error variance for a normal linear regression model, and employs a bootstrap procedure to assess the null hypothesis that the fitted model shows no lack of fit. For more information, see Koeneman and Cavanaugh (2023) <doi:10.48550/arXiv.2309.10614>. Functionality to perform all subsets linear or generalized linear regression is also available.
| Version: | 0.2.0 |
| Depends: | R (≥ 4.1.0) |
| Published: | 2023-09-20 |
| DOI: | 10.32614/CRAN.package.DBModelSelect |
| Author: | Scott H. Koeneman [aut, cre] |
| Maintainer: | Scott H. Koeneman <Scott.Koeneman at jefferson.edu> |
| License: | GPL-3 |
| URL: | https://github.com/shkoeneman/DBModelSelect |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | DBModelSelect results |
| Reference manual: | DBModelSelect.pdf |
| Package source: | DBModelSelect_0.2.0.tar.gz |
| Windows binaries: | r-devel: DBModelSelect_0.2.0.zip, r-release: DBModelSelect_0.2.0.zip, r-oldrel: DBModelSelect_0.2.0.zip |
| macOS binaries: | r-release (arm64): DBModelSelect_0.2.0.tgz, r-oldrel (arm64): DBModelSelect_0.2.0.tgz, r-release (x86_64): DBModelSelect_0.2.0.tgz, r-oldrel (x86_64): DBModelSelect_0.2.0.tgz |
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