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)
<doi:10.48550/arXiv.2309.10614>. Functionality to perform all subsets linear or generalized linear
regression is also available.
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