SamplingBigData: Sampling Methods for Big Data

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.

Version: 1.0.0
Published: 2018-09-03
Author: Jonathan Lisic, Anton Grafström
Maintainer: Jonathan Lisic <jlisic at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/jlisic/SamplingBigData
NeedsCompilation: yes
CRAN checks: SamplingBigData results

Downloads:

Reference manual: SamplingBigData.pdf
Package source: SamplingBigData_1.0.0.tar.gz
Windows binaries: r-devel: SamplingBigData_1.0.0.zip, r-release: SamplingBigData_1.0.0.zip, r-oldrel: SamplingBigData_1.0.0.zip
macOS binaries: r-release: SamplingBigData_1.0.0.tgz, r-oldrel: SamplingBigData_1.0.0.tgz

Reverse dependencies:

Reverse imports: BalancedSampling

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