The R package “NeEDS4BigData” provides approaches to implement subsampling methods to analyse big data.

New Experimental Design based Subsampling methods for Big Data.

## Installing the package from GitHub
devtools::install_github("Amalan-ConStat/NeEDS4BigData")

## Installing the package from CRAN
install.packages("NeEDS4BigData")

1. A- and L-optimality based subsampling for GLMs.
2. A-optimality based subsampling for Gaussian Linear Models.
3. Leverage sampling for GLMs.
4. Local case control sampling for logistic regression.
5. A-optimality based subsampling under measurement constraints for GLMs.
6. Model robust subsampling method for GLMs.
7. Subsampling method for GLMs when the model is potentially misspecified.

These seven methods are described in the following articles

1. Introduction - explains the need for subsampling methods.
2. Linear Regression - Basic sampling.
3. Linear Regression - Model robust and misspecification.
4. Logistic Regression - Basic sampling.
5. Logistic Regression - Model robust and misspecification.
6. Poisson Regression - Basic sampling.
7. Poisson Regression - Model robust and misspecification.

For $2,4$ and $6$ we assume the main effects model can describe the data. While for $3,5$ and $7$ first we consider there are several models that can describe the big data, then later we assume the given main effects model is misspecified. Under these conditions from $2-7$ we explore subsampling for three given big data sets.