Fast Best Subset Selection
Overview
Extremely efficient toolkit for solving the best subset selection problem https://www.jmlr.org/papers/v23/21-1060.html. This package is its R interface. The package implements and generalizes algorithms designed in doi:10.1073/pnas.2014241117 that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, Ising model reconstruction doi:10.1080/01621459.2025.2571245, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection doi:10.1287/ijoc.2022.1241 and sure independence screening doi:10.1111/j.1467-9868.2008.00674.x are also provided.
- Add a new function
slidethat supports Ising model reconstruction - The maintainer's changed from <zhuj37@mail2.sysu.edu.cn> to <zhuj1jqx@gmail.com>.
From NEWS.md in the release
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-08
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- ERROR12 OK, 1 ERROR2026-07-07
- OK13 OK2026-07-01
- ERROR12 OK, 1 ERROR2026-06-30
- OK13 OK2026-06-27
- ERROR12 OK, 1 ERROR2026-06-21
- OK12 OK2026-04-25
- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 22 (1 internal)includes a package overview page
- Examples that run
- 67%6 run in a plain check · 3 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 78%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 23%
Downloads
Repository
- PRs merged (all time)
- 94%
- Median days to close, 50 most recently updated closed issues
- 22
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
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10 development-tooling and community-health practices detected across 6 families in the upstream repository
Checks run against github.com/abess-team/abess on 2026-10-18.
The R CMD check workflow runs on Linux, macOS and Windows
GitHub Pages site last deployed 2026-03-04 · The repository has a site deploy workflow
pkgdown site last built 2026-03-04
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This repository shows signs of AI coding tools.
- Claude Codefirst seen 2026-02-26Commits: at least 33 crediting it in the message
- Claude Opus 4.62 commits · Mar 2026
Dependencies
Installing abess adds 11 packages (8 with compiled code, 11 MB of source tarballs). Heaviest: igraph 4.8 MB, Rcpp 1.8 MB, RcppEigen 1.7 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 0.4.11Latest
- RR 4.5.0 released · 2025-04-11
- 0.4.102025-04-05 · diff ↗
- 0.4.92024-09-09 · diff ↗
- RR 4.4.0 released · 2024-04-24
- 0.4.82023-09-19 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 0.4.72023-02-20 · diff ↗
- unarchivedReturned to CRAN2023-02-19
- archivedRemoved from CRAN2023-02-18configure issues were not corrected in time 'CXX' not set correctly
- 0.4.62022-11-06 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 0.4.52022-03-22 · diff ↗
- 0.4.0
- 0.3.02021-09-03 · diff ↗
Package metadata
- First published
- 2021-04-21
- Total releases
- 11 / 5 yrs
- License
- GPL (>= 3) | file LICENSE OSI
- Minimum R
- ≥ 3.1.0
- Bundled data
- 90 KB / 1 file
- Download size
- 777 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("abess")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1073/pnas.2014241117, 10.1287/ijoc.2022.1241.
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