DBModelSelect: Distribution-Based Model Selection

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
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

Documentation:

Reference manual: DBModelSelect.pdf

Downloads:

Package source: DBModelSelect_0.2.0.tar.gz
Windows binaries: r-prerel: DBModelSelect_0.2.0.zip, r-release: DBModelSelect_0.2.0.zip, r-oldrel: DBModelSelect_0.2.0.zip
macOS binaries: r-prerel (arm64): DBModelSelect_0.2.0.tgz, r-release (arm64): DBModelSelect_0.2.0.tgz, r-oldrel (arm64): DBModelSelect_0.2.0.tgz, r-prerel (x86_64): DBModelSelect_0.2.0.tgz, r-release (x86_64): DBModelSelect_0.2.0.tgz

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