subsampling
0.4.0Optimal 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.
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- OK2026-08-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 67%
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Code & Tests
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3 releases. Pick two to compare their code metrics. R releases are shown for context.
- 0.4.0Latest
- RR 4.6.0 released · 2026-04-24
- 0.3.02026-03-10 · diff ↗
- 0.1.12026-03-10
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2024-11-05
- Total releases
- 3 / 2 yrs
- License
- GPL-3 OSI
- Download size
- 73 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet