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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=subsampling to link to this page.
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