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 |
| 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 (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 depends: | SamplingStrata |
| Reverse imports: | BalancedSampling, sgsR |
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