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.
- Version0.2.0
- R version≥ 4.1.0
- LicenseGPL-3
- Needs compilation?No
- Last release09/20/2023
Documentation
Team
Scott H. Koeneman
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