CRAN: Package NeEDS4BigData

NeEDS4BigData: New Experimental Design Based Subsampling Methods for Big Data

Subsampling methods for big data under different models and assumptions. Starting with linear regression and leading to Generalised Linear Models, softmax regression, and quantile regression. Specifically, the model-robust subsampling method proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>, where multiple models can describe the big data, and the subsampling framework for potentially misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025) <doi:10.48550/arXiv.2510.05902>.

Version: 1.0.1

Depends: R (≥ 4.1.0)

Imports: dplyr, foreach, gam, ggh4x, ggplot2, ggridges, matrixStats, mvnfast, psych, Rdpack, Rfast, rlang, stats, tidyr

Suggests: doParallel, ggpubr, kableExtra, knitr, parallel, rmarkdown, spelling, testthat, vctrs, pillar

Published: 2025年10月22日

DOI: 10.32614/CRAN.package.NeEDS4BigData

Author: Amalan Mahendran ORCID iD [aut, cre]

Maintainer: Amalan Mahendran <amalan0595 at gmail.com>

BugReports: https://github.com/Amalan-ConStat/NeEDS4BigData/issues

License: MIT + file LICENSE

URL: https://github.com/Amalan-ConStat/NeEDS4BigData,https://amalan-constat.github.io/NeEDS4BigData/index.html

NeedsCompilation: no

Language: en-GB

CRAN checks: NeEDS4BigData results

Documentation:

Reference manual: NeEDS4BigData.html , NeEDS4BigData.pdf

Downloads:

Package source: NeEDS4BigData_1.0.1.tar.gz

Windows binaries: r-devel: NeEDS4BigData_1.0.1.zip, r-release: NeEDS4BigData_1.0.1.zip, r-oldrel: NeEDS4BigData_1.0.1.zip

macOS binaries: r-release (arm64): NeEDS4BigData_1.0.1.tgz, r-oldrel (arm64): NeEDS4BigData_1.0.1.tgz, r-release (x86_64): NeEDS4BigData_1.0.1.tgz, r-oldrel (x86_64): NeEDS4BigData_1.0.1.tgz

Linking:

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