icmm
1.2Empirical Bayes Variable Selection via ICM/M Algorithm
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- References docs
- 10%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.2Latest
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.12017-10-12 · diff ↗
- 1.02017-07-27
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-07-27
- Total releases
- 3 / 9 yrs
- License
- GPL (>= 2) OSI
- Bundled data
- 913 KB / 7 files
- Download size
- 924 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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