DBModelSelect-package

DBModelSelect

tools:::Rd_package_description("DBModelSelect")

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.

Scott H. Koeneman

Distribution-Based Model Selection

DBModelSelect-package function

Maintainer: Scott H. Koeneman <a href="/link/Scott.Koeneman%40jefferson.edu?package=DBModelSelect&version=0.2.0" data-mini-rdoc="DBModelSelect::Scott.Koeneman@jefferson.edu">Scott.Koeneman@jefferson.edu</a>

Author

tools:::Rd_package_title("DBModelSelect") — DBModelSelect-package

Maintainer: Scott H. Koeneman <a href='mailto:Scott.Koeneman@jefferson.edu'>Scott.Koeneman@jefferson.edu</a>

DBModelSelect-package: tools:::Rd_package_title("DBModelSelect")

Description

Arguments

Author

Details

See Also