https://github.com/krlmlr/wrswor

A package with different implementations of weighted random sampling without replacement in R
https://github.com/krlmlr/wrswor

Last synced: about 1 month ago
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A package with different implementations of weighted random sampling without replacement in R

• Host: GitHub
• URL: https://github.com/krlmlr/wrswor
• Owner: krlmlr
• Created: 2013-03-05T08:11:44.000Z (about 12 years ago)
• Default Branch: main
• Last Pushed: 2024-01-24T00:27:37.000Z (over 1 year ago)
• Last Synced: 2024-04-24T14:08:55.547Z (about 1 year ago)
• Language: TeX
• Homepage: https://krlmlr.github.io/wrswoR
• Size: 24 MB
• Stars: 17
• Watchers: 2
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

pkgload::load_all()

```

# wrswoR

[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)

[![R build status](https://github.com/krlmlr/wrswoR/workflows/rcc/badge.svg)](https://github.com/krlmlr/wrswoR/actions)

[![Codecov test coverage](https://codecov.io/gh/krlmlr/wrswoR/branch/main/graph/badge.svg)](https://codecov.io/gh/krlmlr/wrswoR?branch=main)

[![CRAN status](https://www.r-pkg.org/badges/version/wrswoR)](https://CRAN.R-project.org/package=wrswoR)

The goal of wrswoR is to provide faster implementations of weighted random sampling without replacement in R.

## Installation

You can install the released version of wrswoR from [CRAN](https://CRAN.R-project.org) with:

``` r

install.packages("wrswoR")

```

And the development version from [GitHub](https://github.com/) with:

``` r

# install.packages("devtools")

devtools::install_github("krlmlr/wrswoR")

```

## Example

The functions in this package are a drop-in replacement to `sample.int(n, size, replace = FALSE, prob = prob)`.

With large `n`, `sample.int()` becomes too slow to be practical, unlike the functions in this package.

```{r example}

library(wrswoR)

set.seed(20200726)

sample_int_crank(20, 10, 1:20)

```