Package: DBModelSelect
Type: Package
Title: Distribution-Based Model Selection
Version: 0.2.0
Date: 2023-08-22
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
NeedsCompilation: no
Packaged: 2023-09-20 13:38:19 UTC; syk070
Author: Scott H. Koeneman [aut, cre]
Maintainer: Scott H. Koeneman <Scott.Koeneman@jefferson.edu>
Repository: CRAN
Date/Publication: 2023-09-20 18:40:08 UTC
Built: R 4.3.3; ; 2025-01-24 06:24:53 UTC; unix
