Package: DBModelSelect 0.2.1

Scott H. Koeneman

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) <arxiv:2309.10614>. Functionality to perform all subsets linear or generalized linear regression is also available.

Authors:Scott H. Koeneman [aut, cre]

DBModelSelect_0.2.1.tar.gz
DBModelSelect_0.2.1.zip(r-4.5)DBModelSelect_0.2.1.zip(r-4.4)DBModelSelect_0.2.1.zip(r-4.3)
DBModelSelect_0.2.1.tgz(r-4.4-any)DBModelSelect_0.2.1.tgz(r-4.3-any)
DBModelSelect_0.2.1.tar.gz(r-4.5-noble)DBModelSelect_0.2.1.tar.gz(r-4.4-noble)
DBModelSelect_0.2.1.tgz(r-4.4-emscripten)DBModelSelect_0.2.1.tgz(r-4.3-emscripten)
DBModelSelect.pdf |DBModelSelect.html✨
DBModelSelect/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/shkoeneman/dbmodelselect/issues

On CRAN:

5 exports 0.93 score 0 dependencies 2 scripts 255 downloads

Last updated 6 months agofrom:29e5d48ebd. Checks:OK: 7. Indexed: yes.

Exports:AICcBootGOFTestLMFitGLMSubsetsFitLMSubsetsStandICModelSelect

Dependencies: