Selection Threshold Optimized Empirically via Splitting
Overview
Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (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
- 4
- Examples that run
- 100%4 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 25%
Downloads
Dependencies
Installing STOPES adds 11 packages (6 with compiled code, 10 MB of source tarballs). Heaviest: glmnet 2.7 MB, robustbase 2.2 MB, Rcpp 1.8 MB.
Code & Tests
People & History
Pick two releases to compare their code metrics.
- 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
- 0.2Latest
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.12019-06-14
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2019-06-14
- Total releases
- 2 / 7 yrs
- License
- GPL-2 OSI
- Download size
- 5.8 KB
Cite
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Run in R for the authors' preferred citation:
citation("STOPES")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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