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.

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
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:

Reference manual:	subsampling.pdf
Vignettes:	Introduction to 'ssp.glm': Subsampling for Generalized Linear Models (source, R code) Introduction to 'ssp.quantreg': Subsampling for Quantile Regression (source, R code) Introduction to 'ssp.relogit': Subsampling for Logistic Regression Model with Rare Events (source, R code) Introduction to 'ssp.softmax': Subsampling for Softmax (Multinomial) Regression Model (source, R code)

Downloads:

Package source:	subsampling_0.1.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): subsampling_0.1.1.tgz, r-oldrel (arm64): subsampling_0.1.1.tgz, r-release (x86_64): subsampling_0.1.1.tgz, r-oldrel (x86_64): subsampling_0.1.1.tgz

Linking:

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