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maclogp  

Measures of Uncertainty for Model Selection
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install.packages("maclogp")

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library("remotes")
install_github("cran/maclogp")

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library("remotes")
install_version("maclogp", "0.1.1")



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library("maclogp")
Maintained by
Yuanyuan Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-04-22
Latest Update: 2021-04-22
Description:
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) . 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.
How to cite:
Yuanyuan Li (2021). maclogp: Measures of Uncertainty for Model Selection. R package version 0.1.1, https://cran.r-project.org/web/packages/maclogp. Accessed 03 Dec. 2024.
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Complete documentation for maclogp
Functions, R codes and Examples using the maclogp R package
Some associated functions: MAC . Models_gen . bms . diabetes . plot_MAC . 
Some associated R codes: MAC.R . Models_gen.R . bms.R . data.R . plot_MAC.R . subfuncs.R .  Full maclogp package functions and examples
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