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
DOI:	10.32614/CRAN.package.SamplingBigData
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

Documentation:

Reference manual:

SamplingBigData.html , SamplingBigData.pdf

Downloads:

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 (arm64): SamplingBigData_1.0.0.tgz, r-oldrel (arm64): SamplingBigData_1.0.0.tgz, r-release (x86_64): SamplingBigData_1.0.0.tgz, r-oldrel (x86_64): SamplingBigData_1.0.0.tgz

Reverse dependencies:

Reverse imports:

SamplingStrata, sgsR

Linking:

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