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.
|Author:||Jonathan Lisic, Anton Grafström|
|Maintainer:||Jonathan Lisic <jlisic at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|CRAN checks:||SamplingBigData results|
|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 imports:||BalancedSampling, sgsR|
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