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.
| Version: | 0.3.0 |
| Imports: | expm, nnet, quantreg, Rcpp (≥ 1.0.12), stats, survey |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, MASS, rmarkdown, tinytest |
| Published: | 2026-03-10 |
| DOI: | 10.32614/CRAN.package.subsampling |
| Author: | Qingkai Dong [aut, cre, cph], Yaqiong Yao [aut], Haiying Wang [aut], Qiang Zhang [ctb], Jun Yan [ctb] |
| Maintainer: | Qingkai Dong <qingkai.dong at uconn.edu> |
| BugReports: | https://github.com/dqksnow/Subsampling/issues |
| License: | GPL-3 |
| URL: | https://github.com/dqksnow/subsampling, https://dqksnow.github.io/subsampling/ |
| NeedsCompilation: | yes |
| Materials: | README, NEWS |
| CRAN checks: | subsampling results |
| Package source: | subsampling_0.3.0.tar.gz |
| Windows binaries: | r-devel: subsampling_0.1.1.zip, r-release: subsampling_0.3.0.zip, r-oldrel: subsampling_0.3.0.zip |
| macOS binaries: | r-release (arm64): subsampling_0.3.0.tgz, r-oldrel (arm64): subsampling_0.3.0.tgz, r-release (x86_64): subsampling_0.3.0.tgz, r-oldrel (x86_64): subsampling_0.3.0.tgz |
| Old sources: | subsampling archive |
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