Package: subsampling 0.3.0
subsampling: Optimal Subsampling Methods for Statistical Models
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.
Authors:
subsampling_0.3.0.tar.gz
subsampling_0.3.0.tar.gz(r-4.7-arm64)subsampling_0.3.0.tar.gz(r-4.7-x86_64)subsampling_0.3.0.tar.gz(r-4.6-arm64)subsampling_0.3.0.tar.gz(r-4.6-x86_64)
subsampling_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
subsampling/json (API)
NEWS
| # Install 'subsampling' in R: |
| install.packages('subsampling', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dqksnow/subsampling/issues
Pkgdown/docs site:https://dqksnow.github.io
Last updated from:b00f43b2e5. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 156 | ||
| linux-devel-x86_64 | OK | 186 | ||
| source / vignettes | OK | 222 | ||
| linux-release-arm64 | OK | 162 | ||
| linux-release-x86_64 | OK | 152 | ||
| wasm-release | OK | 117 |
Exports:ssp.glmssp.glm.rFssp.quantregssp.relogitssp.softmax
Dependencies:DBIexpmlatticeMASSMatrixMatrixModelsminqamitoolsnnetnumDerivquantregRcppRcppArmadilloSparseMsurveysurvival
ssp.glm.rF: Balanced Subsampling for Preserving Rare Features in Generalized Linear Models
Rendered fromssp-logit-rF.Rmdusingknitr::rmarkdownon Jun 08 2026.Last update: 2026-03-10
Started: 2026-03-10
ssp.glm: Subsampling for Generalized Linear Models
Rendered fromssp-logit.Rmdusingknitr::rmarkdownon Jun 08 2026.Last update: 2026-03-10
Started: 2024-11-05
ssp.quantreg: Subsampling for Quantile Regression
Rendered fromssp-quantreg.Rmdusingknitr::rmarkdownon Jun 08 2026.Last update: 2026-03-10
Started: 2024-11-05
ssp.relogit: Subsampling for Logistic Regression Model with Rare Events
Rendered fromssp-relogit.Rmdusingknitr::rmarkdownon Jun 08 2026.Last update: 2026-03-10
Started: 2024-11-05
ssp.softmax: Subsampling for Softmax (Multinomial) Regression Model
Rendered fromssp-softmax.Rmdusingknitr::rmarkdownon Jun 08 2026.Last update: 2026-03-10
Started: 2024-11-05
