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NeEDS4BigData  

New Experimental Design Based Subsampling Methods for Big Data
View on CRAN: Click here


Download and install NeEDS4BigData package within the R console
Install from CRAN:
install.packages("NeEDS4BigData")

Install from Github:
library("remotes")
install_github("cran/NeEDS4BigData")

Install by package version:
library("remotes")
install_version("NeEDS4BigData", "1.0.1")



Attach the package and use:
library("NeEDS4BigData")
Maintained by
Amalan Mahendran
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-10-22
Latest Update: 2025-10-22
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>.
How to cite:
Amalan Mahendran (2025). NeEDS4BigData: New Experimental Design Based Subsampling Methods for Big Data. R package version 1.0.1, https://cran.r-project.org/web/packages/NeEDS4BigData. Accessed 06 Jun. 2026.
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Full NeEDS4BigData package functions and examples
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