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.
- Version0.1.1
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release04/22/2021
Documentation
Team
Yuanyuan Li
Jiming Jiang
Show author detailsRolesThesis advisor
Insights
Last 30 days
This package has been downloaded 120 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.
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Last 365 days
This package has been downloaded 1,593 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 22 downloads.
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Dependencies
- Imports3 packages