Package: SamplingBigData 1.0.0

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.

Authors:Jonathan Lisic, Anton Grafström

SamplingBigData_1.0.0.tar.gz
SamplingBigData_1.0.0.zip(r-4.7)SamplingBigData_1.0.0.zip(r-4.6)SamplingBigData_1.0.0.zip(r-4.5)
SamplingBigData_1.0.0.tgz(r-4.6-x86_64)SamplingBigData_1.0.0.tgz(r-4.6-arm64)SamplingBigData_1.0.0.tgz(r-4.5-x86_64)SamplingBigData_1.0.0.tgz(r-4.5-arm64)
SamplingBigData_1.0.0.tar.gz(r-4.7-arm64)SamplingBigData_1.0.0.tar.gz(r-4.7-x86_64)SamplingBigData_1.0.0.tar.gz(r-4.6-arm64)SamplingBigData_1.0.0.tar.gz(r-4.6-x86_64)
SamplingBigData_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SamplingBigData/json (API)

# Install 'SamplingBigData' in R:
install.packages('SamplingBigData', repos = c('https://jlisic.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jlisic/samplingbigdata/issues

On CRAN:

Conda:

4.52 score 10 stars 2 packages 11 scripts 422 downloads 2 exports 0 dependencies

Last updated from:3b89b9152f. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK110
linux-devel-x86_64OK102
source / vignettesOK134
linux-release-arm64OK104
linux-release-x86_64OK93
macos-release-arm64OK116
macos-release-x86_64OK206
macos-oldrel-arm64OK126
macos-oldrel-x86_64OK316
windows-develOK78
windows-releaseOK75
windows-oldrelOK63
wasm-releaseOK92

Exports:lpm2_kdtreesplit_sample

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Sampling Methods for Big DataSamplingBigData-package SamplingBigData
Local Pivotal Methodlpm2_kdtree
Split Samplesplit_sample