New Experimental Design Based Subsampling Methods for Big Data R package NeEDS4BigData version 1.0.1 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>. Software 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 Amalan Mahendran <amalan0595@gmail.com> Comprehensive R Archive Network (CRAN) Amalan Mahendran [aut, cre] (ORCID: <https://orcid.org/0000-0002-0643-9052>) MIT + file LICENSE (https://CRAN.R-project.org/package=NeEDS4BigData/LICENSE) 2025-10-22 application/tgz https://CRAN.R-project.org/package=NeEDS4BigData doi:10.32614/CRAN.package.NeEDS4BigData en-GB