Package: subsampling 0.1.1

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.

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

subsampling_0.1.1.tar.gz
subsampling_0.1.1.tar.gz(r-4.5-noble)subsampling_0.1.1.tar.gz(r-4.4-noble)
subsampling_0.1.1.tgz(r-4.4-emscripten)subsampling_0.1.1.tgz(r-4.3-emscripten)
subsampling.pdf |subsampling.html
subsampling/json (API)
NEWS

# Install 'subsampling' in R:
install.packages('subsampling', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dqksnow/subsampling/issues

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

openblascpp

3.30 score 6 scripts 443 downloads 4 exports 16 dependencies

Last updated 1 months agofrom:897268b17f. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linux-x86_64OKDec 06 2024

Exports:ssp.glmssp.quantregssp.relogitssp.softmax

Dependencies:DBIexpmlatticeMASSMatrixMatrixModelsminqamitoolsnnetnumDerivquantregRcppRcppArmadilloSparseMsurveysurvival

Introduction to ssp.glm: Subsampling for Generalized Linear Models

Rendered fromssp-logit.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-11-05
Started: 2024-11-05

Introduction to ssp.quantreg: Subsampling for Quantile Regression

Rendered fromssp-quantreg.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-11-05
Started: 2024-11-05

Introduction to ssp.relogit: Subsampling for Logistic Regression Model with Rare Events

Rendered fromssp-relogit.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-11-05
Started: 2024-11-05

Introduction to ssp.softmax: Subsampling for Softmax (Multinomial) Regression Model

Rendered fromssp-softmax.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-11-05
Started: 2024-11-05