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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) <arXiv:2309.10614>. Functionality to perform all subsets linear or generalized linear regression is also available.

Versions across snapshots

VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 DBModelSelect_0.2.0.tar.gz 38.5 KiB
0.2.0 rolling linux/noble R-4.5 DBModelSelect_0.2.0.tar.gz 38.4 KiB
0.2.0 rolling source/ R- DBModelSelect_0.2.0.tar.gz 12.0 KiB
0.2.0 latest linux/jammy R-4.5 DBModelSelect_0.2.0.tar.gz 38.5 KiB
0.2.0 latest linux/noble R-4.5 DBModelSelect_0.2.0.tar.gz 38.4 KiB
0.2.0 latest source/ R- DBModelSelect_0.2.0.tar.gz 12.0 KiB
0.2.0 2026-04-26 source/ R- DBModelSelect_0.2.0.tar.gz 12.0 KiB
0.2.0 2026-04-23 source/ R- DBModelSelect_0.2.0.tar.gz 12.0 KiB
0.2.0 2026-04-09 windows/windows R-4.5 DBModelSelect_0.2.0.zip 41.7 KiB
0.2.0 2025-04-20 source/ R- DBModelSelect_0.2.0.tar.gz 12.0 KiB