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: |
2025-10-22 |
| DOI: |
10.32614/CRAN.package.NeEDS4BigData |
| Author: |
Amalan Mahendran 
[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:
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