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Empirical Bayes Variable Selection via ICM/M Algorithm

0packages depend
2.4Kdownloads / year
–test coverage
8/13flavors OK

Overview

About

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.

Changes in 1.2
  • Fix bug for option ising.prior = TRUE in the main function icmm.

From NEWS.md in the release

Install

Health

CRAN checks
5NOTE8OK
Check messages
NOTEdependencies in R coder-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64

r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Base package in Suggests/Enhances imported in NAMESPACE:
  ‘stats’

r-devel-windows-x86_64

Base package in Suggests/Enhances imported in NAMESPACE:
  'stats'

CRAN check page ↗

Slowest check: 1.0 min · r-oldrel-windows-x86_64
Status changes
  • NOTE8 OK, 5 NOTENow
  • NOTE11 OK, 2 NOTE2026-09-12
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.2
READMENoVignettesNoNEWSYes · 67% structured · mentions 1.2Citation fileNoWebsitenot declared in DESCRIPTION
Help pages
20includes a package overview page
Examples that run
100%19 run in a plain check
Exported functions with examples
100%
Documented parameters
not tracked
Return-value docs
not tracked
Help pages citing references
10%

Downloads

2.4K
CRAN downloads in the past year
Rank #19,472 · ~6/day · ~197/mo
Daily download trend is not available in this view yet.
26730 days
67690 days
2.4K1 year
Compare downloads with other packages →
Also on71 r2u5 autocran

Dependencies

Declared dependencies
1 external dependency (excludes base and recommended)

Installing icmm adds 2 packages (1 with compiled code, 1.2 MB of source tarballs). Heaviest: EbayesThresh 692 KB, wavethresh 567 KB.

Depends (0)
none
Imports (1)
LinkingTo (0)
none
Suggests (2)
MASSstats
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (3)
Author, Maintainer since 1.0 (2017)
Contributor
Contributor
Releases (3)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • 1.2Latest
    record
    2021-05-26 · current release · diff ↗
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
  • 1.1
    record
    2017-10-12 · diff ↗
  • 1.0
    record
    2017-07-27
  • R
    R 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

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("icmm")
Pungpapong, V., Zhang, D., & Zhang, M. (2021). icmm: Empirical Bayes Variable Selection via ICM/M Algorithm (Version 1.2) [Computer software]. https://doi.org/10.32614/CRAN.package.icmm

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for icmm version 1.2 [Data set]. HJJB, LLC. Data release v2026-10-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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