Distribution-Based Model Selection R package DBModelSelect version 0.2.0 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. Software Depends: R (>= 4.1.0) Scott H. Koeneman <Scott.Koeneman@jefferson.edu> Comprehensive R Archive Network (CRAN) Scott H. Koeneman [aut, cre] GPL-3 2023-09-20 application/tgz https://CRAN.R-project.org/package=DBModelSelect doi:10.32614/CRAN.package.DBModelSelect