Optimization via Subsampling (OPTS)
Overview
Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 0%
Downloads
Dependencies
Installing OPTS adds 5 packages (3 with compiled code, 3.2 MB of source tarballs). Heaviest: robustbase 2.2 MB, zoo 675 KB, changepoint 274 KB.
Code & Tests
People & History
- 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
- 0.1Latest2022-05-25 · current release
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-05-25
- Total releases
- 1 / 4 yrs
- License
- GPL-2 OSI
- Download size
- 3.8 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("OPTS")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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