Weighted Random Sampling without Replacement

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


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A package with different implementations of weighted random sampling without replacement in R.

library(devtools)
install_github("krlmlr/wrswoR")

News

wrswoR 1.1 (2018-02-02)

  • Use microbenchmark package conditionally.
  • Remove dependency on cluster.
  • Use native method registration.
  • Prepare for submission to R Journal.
  • Improve vignette.
  • Internal tweaks.

Version 1.0-1 (2016-02-26)

  • Finally submitted version of the paper.

Version 1.0 (2016-02-22)

First CRAN release.

  • Alternative implementations for weighted random sampling, as implemented by R's sample.int(replace = FALSE, prob = ...)
    • sample_int_rej(): A rejective algorithm, in R
    • sample_int_rank(): One-pass sampling by Efraimidis and Spirakis, in R
    • sample_int_crank(): One-pass sampling by Efraimidis and Spirakis, in C++
    • sample_int_expj(): Reservoir sampling with exponential jumps Efraimidis and Spirakis, in C++
    • Two more experimental functions.
  • Article for submission to JStatSoft as vignette.

Reference manual

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install.packages("wrswoR")

1.1 by Kirill Müller, 5 months ago


http://krlmlr.github.io/wrswoR


Report a bug at https://github.com/krlmlr/wrswoR/issues


Browse source code at https://github.com/cran/wrswoR


Authors: Kirill Müller [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports logging, Rcpp

Suggests BatchExperiments, dplyr, ggplot2, import, kimisc, knitcitations, knitr, metap, microbenchmark, rmarkdown, roxygen2, rticles, sampling, testthat, tidyr, tikzDevice

Linking to Rcpp


Imported by rakeR, simPop.


See at CRAN