WaveSampling: Weakly Associated Vectors (WAVE) Sampling

Spatial data are generally auto-correlated, meaning that if two units selected are close to each other, then it is likely that they share the same properties. For this reason, when sampling in the population it is often needed that the sample is well spread over space. A new method to draw a sample from a population with spatial coordinates is proposed. This method is called wave (Weakly Associated Vectors) sampling. It uses the less correlated vector to a spatial weights matrix to update the inclusion probabilities vector into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <arXiv:1910.13152>.

Version: 0.1.1
Depends: Matrix, R (≥ 2.10)
Imports: Rcpp
LinkingTo: RcppArmadillo, Rcpp
Suggests: knitr, rmarkdown, ggplot2, ggvoronoi, sampling, BalancedSampling, sp, sf, stats
Published: 2020-01-30
Author: Raphaël Jauslin ORCID iD [aut, cre], Yves Tillé ORCID iD [aut]
Maintainer: Raphaël Jauslin <raphael.jauslin at unine.ch>
BugReports: https://github.com/RJauslin/WaveSampling/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/RJauslin/WaveSampling
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: WaveSampling results

Downloads:

Reference manual: WaveSampling.pdf
Vignettes: Introduction
Package source: WaveSampling_0.1.1.tar.gz
Windows binaries: r-devel: WaveSampling_0.1.1.zip, r-release: WaveSampling_0.1.1.zip, r-oldrel: WaveSampling_0.1.1.zip
macOS binaries: r-release: WaveSampling_0.1.1.tgz, r-oldrel: WaveSampling_0.1.1.tgz
Old sources: WaveSampling archive

Reverse dependencies:

Reverse imports: SpotSampling

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