Package: maclogp 0.1.1

maclogp: Measures of Uncertainty for Model Selection

Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.

Authors:Yuanyuan Li [aut, cre], Jiming Jiang [ths]

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maclogp.pdf |maclogp.html
maclogp/json (API)

# Install 'maclogp' in R:
install.packages('maclogp', repos = c('https://yuanyuanli96.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/yuanyuanli96/maclogp/issues

Datasets:

On CRAN:

2.70 score 1 stars 1 scripts 108 downloads 4 exports 17 dependencies

Last updated 2 years agofrom:6e8e329a53. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 16 2024
R-4.5-winOKOct 16 2024
R-4.5-linuxOKOct 16 2024
R-4.4-winOKOct 16 2024
R-4.4-macOKOct 16 2024
R-4.3-winOKOct 16 2024
R-4.3-macOKOct 16 2024

Exports:bmsMACModels_genplot_MAC

Dependencies:BMAdata.tableDEoptimRinlinejsonlitelatticeleapsMatrixmvtnormpcaPPplot.matrixrlistrobustbaserrcovsurvivalXMLyaml