Package: DBModelSelect
Type: Package
Title: Distribution-Based Model Selection
Version: 0.2.1
Date: 2023-09-25
Authors@R: person("Scott H.", "Koeneman", 
                  email = "Scott.Koeneman@jefferson.edu", 
                  role = c("aut", "cre"))
Description: 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.
URL: https://github.com/shkoeneman/DBModelSelect
License: GPL-3
Depends: R (>= 4.1.0)
RoxygenNote: 7.2.3
Repository: https://shkoeneman.r-universe.dev
Date/Publication: 2024-03-14 18:35:26 UTC
RemoteUrl: https://github.com/shkoeneman/dbmodelselect
RemoteRef: HEAD
RemoteSha: 29e5d48ebd4e1417188b7b3a1cb865ec681db342
NeedsCompilation: no
Packaged: 2026-09-21 10:43:38 UTC; root
Author: Scott H. Koeneman [aut, cre]
Maintainer: Scott H. Koeneman <Scott.Koeneman@jefferson.edu>