DESCRIPTION

Type: Package
Package: NeEDS4BigData
Title: New Experimental Design Based Subsampling Methods for Big Data
Version: 1.0.0
Authors@R: person(given = "Amalan", family = "Mahendran", email = "amalan0595@gmail.com", role = c("aut", "cre"),comment = c(ORCID = "0000-0002-0643-9052"))
Maintainer: The package maintainer <yourself@somewhere.net>
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. Specially, the model robust subsampling method 
    proposed by Mahendran et al (2023) (<doi:10.1007/s00362-023-01446-9>) when multiple 
    models can describe the big data.
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 (>= 4.0.0)
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
Encoding: UTF-8
Language: en-GB
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.1
Config/testthat/edition: 3