Package: subsampling 0.3.0

Qingkai Dong

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:Qingkai Dong [aut, cre, cph], Yaqiong Yao [aut], Haiying Wang [aut], Qiang Zhang [ctb], Jun Yan [ctb]

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.40 score 7 scripts 595 downloads 5 exports 16 dependencies

Last updated from:b00f43b2e5. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK156
linux-devel-x86_64OK186
source / vignettesOK222
linux-release-arm64OK162
linux-release-x86_64OK152
wasm-releaseOK117

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