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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.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 depends: SamplingStrata
Reverse imports: sgsR

Linking:

Please use the canonical form https://CRAN.R-project.org/package=SamplingBigData to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.