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wrswoR: Weighted Random Sampling without Replacement

A collection of implementations of classical and novel algorithms for weighted sampling without replacement.

Version: 1.1.1
Depends: R (≥ 3.0.2)
Imports: logging (≥ 0.4-13), Rcpp
LinkingTo: Rcpp (≥ 0.11.5)
Suggests: BatchExperiments, BiocManager, dplyr, ggplot2, import, kimisc (≥ 0.2-4), knitcitations, knitr, metap, microbenchmark, rmarkdown, roxygen2, rticles (≥ 0.1), sampling, testthat, tidyr, tikzDevice (≥ 0.9-1)
Published: 2020-07-26
DOI: 10.32614/CRAN.package.wrswoR
Author: Kirill Müller [aut, cre]
Maintainer: Kirill Müller <krlmlr+r at mailbox.org>
BugReports: https://github.com/krlmlr/wrswoR/issues
License: GPL-3
URL: http://krlmlr.github.io/wrswoR
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: wrswoR results

Documentation:

Reference manual: wrswoR.pdf

Downloads:

Package source: wrswoR_1.1.1.tar.gz
Windows binaries: r-devel: wrswoR_1.1.1.zip, r-release: wrswoR_1.1.1.zip, r-oldrel: wrswoR_1.1.1.zip
macOS binaries: r-release (arm64): wrswoR_1.1.1.tgz, r-oldrel (arm64): wrswoR_1.1.1.tgz, r-release (x86_64): wrswoR_1.1.1.tgz, r-oldrel (x86_64): wrswoR_1.1.1.tgz
Old sources: wrswoR archive

Reverse dependencies:

Reverse imports: MEB, mlfit, rakeR, simPop
Reverse suggests: mvgam, singleCellHaystack

Linking:

Please use the canonical form https://CRAN.R-project.org/package=wrswoR 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.