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SamplingBigData  

Sampling Methods for Big Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("SamplingBigData", "1.0.0")



Attach the package and use:
library("SamplingBigData")
Maintained by
Jonathan Lisic
[Scholar Profile | Author Map]
First Published: 2018-09-03
Latest Update: 2018-09-03
Description:
Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.
How to cite:
Jonathan Lisic (2018). SamplingBigData: Sampling Methods for Big Data. R package version 1.0.0, https://cran.r-project.org/web/packages/SamplingBigData. Accessed 30 Apr. 2025.
Previous versions and publish date:
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Complete documentation for SamplingBigData
Functions, R codes and Examples using the SamplingBigData R package
Some associated functions: SamplingBigData-package . lpm2_kdtree . split_sample . 
Some associated R codes: lpm2_kdtree.R . split.R .  Full SamplingBigData package functions and examples
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