Optimal Subsampling Methods for Statistical Models
Overview
Balancing computational and statistical efficiency, subsampling techniques offer a practical solution for handling large-scale data analysis. Subsampling methods enhance statistical modeling for massive datasets by efficiently drawing representative subsamples from full dataset based on tailored sampling probabilities. These probabilities are optimized for specific goals, such as minimizing the variance of coefficient estimates or reducing prediction error. Based on specified modeling assumptions and subsampling techniques, the package provides functions to draw subsamples from the full data, fit the model on the subsamples, and perform statistical inference.
- Expanded
ssp.glm.rF()with a more comprehensive rare-feature subsampling implementation, including additional criteria, automatic rare-feature detection, response balancing options, and union-based combination. - Improved summaries for rare-feature GLMs and rare-event logistic regression, including clearer sample-composition information.
- Added broader tests for rare-feature GLM scenarios and updated rare-event logistic regression summary tests.
- Revised package vignettes, including the rare-feature GLM vignette and wording cleanup across method vignettes.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-04-22
Show 2 earlier changesHide 2 earlier changes
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 6includes a package overview page
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 67%
- `ssp.glm.rF`: Balanced Subsampling for Preserving Rare Features in Generalized Linear Models HTML
- `ssp.glm`: Optimal Subsampling for Generalized Linear Models HTML
- `ssp.quantreg`: Subsampling for Quantile Regression HTML
- `ssp.relogit`: Optimal Subsampling for Logistic Regression with Rare Events HTML
- `ssp.softmax`: Subsampling for Softmax (Multinomial) Regression Model HTML
- Algorithmic Structure of Optimal Subsampling HTML
Downloads
Repository
- PRs merged (all time)
- not measured
- Median days to close, 50 most recently updated closed issues
- 82
- Median days to close, 50 most recently updated closed PRs
- not measured
Stars over time
Issues over time
8 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/dqksnow/subsampling on 2026-10-22.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2026-06-22
pkgdown site last built 2025-05-12, before release 0.4.0 (2026-06-21)
Dependencies
Installing subsampling adds 11 packages (7 with compiled code, 7.8 MB of source tarballs). Heaviest: survey 2.3 MB, Rcpp 1.8 MB, RcppArmadillo 1.0 MB.
Code & Tests
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- 0.4.0Latest
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- 0.3.0
- RR 4.5.0 released · 2025-04-11
- 0.1.12024-11-05
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-11-05
- Total releases
- 3 / 2 yrs
- License
- GPL-3 OSI
- Download size
- 131 KB
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