subsampling: Optimal Subsampling Methods for Statistical Models (original) (raw)
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.
| Version: | 0.1.1 |
|---|---|
| Imports: | expm, nnet, quantreg, Rcpp (≥ 1.0.12), stats, survey |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, MASS, rmarkdown, tinytest |
| Published: | 2024-11-05 |
| 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 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | subsampling results |
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