icmm
Empirical Bayes Variable Selection via ICM/M Algorithm
Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.
- Version1.2
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release05/26/2021
Documentation
Team
Vitara Pungpapong
Dabao Zhang
Show author detailsRolesContributorMin Zhang
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 165 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
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Last 365 days
This package has been downloaded 2,083 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 77 downloads.
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Dependencies
- Imports1 package
- Suggests1 package