CRAN/E | DBModelSelect

DBModelSelect

Distribution-Based Model Selection

Installation

About

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) . Functionality to perform all subsets linear or generalized linear regression is also available.

github.com/shkoeneman/DBModelSelect

Key Metrics

Version 0.2.0
R ≥ 4.1.0
Published 2023-09-20 213 days ago
Needs compilation? no
License GPL-3
CRAN checks DBModelSelect results

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Maintainer

Maintainer

Scott H. Koeneman

Scott.Koeneman@jefferson.edu

Authors

Scott H. Koeneman

aut / cre

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 4.1.0