Type: Package
Package: NeEDS4BigData
Title: New Experimental Design Based Subsampling Methods for Big Data
Version: 1.0.1
Authors@R: person(given = "Amalan", family = "Mahendran", email = "amalan0595@gmail.com", role = c("aut", "cre"),comment = c(ORCID = "0000-0002-0643-9052"))
Maintainer: Amalan Mahendran <amalan0595@gmail.com>
Description: 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>.
License: MIT + file LICENSE
URL:
        https://github.com/Amalan-ConStat/NeEDS4BigData,https://amalan-constat.github.io/NeEDS4BigData/index.html
BugReports: https://github.com/Amalan-ConStat/NeEDS4BigData/issues
Depends: R (>= 4.1.0)
Imports: dplyr, foreach, gam, ggh4x, ggplot2, ggridges, matrixStats,
        mvnfast, psych, Rdpack, Rfast, rlang, stats, tidyr
RdMacros: Rdpack
Suggests: doParallel, ggpubr, kableExtra, knitr, parallel, rmarkdown,
        spelling, testthat, vctrs, pillar
Encoding: UTF-8
Language: en-GB
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.1
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-10-18 14:12:20 UTC; amala
Author: Amalan Mahendran [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-0643-9052>)
Repository: CRAN
Date/Publication: 2025-10-22 19:00:08 UTC
Built: R 4.4.3; ; 2025-12-22 04:08:53 UTC; windows
