# Distributions in R

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One of the R language’s most powerful features is its ability to deal with random distributions: not just generating random numbers from various distributions (based on a very powerful pseudo-random number generator), but also calculating densities, probabilities, and quintiles. John Cook provides a handy reference chart listing all of the distributions supported by standard R (reproduced below — and there are many other distributions supported by contributed packages), and also explains the elegant naming scheme for the various functions.

Distribution |
Base name |
Parameters |

beta | `beta` |
`shape1` , `shape2, ncp` |

binomial | `binom` |
`size` , `prob` |

Cauchy | `cauchy` |
`location` , `scale` |

chi-squared | `chisq` |
`df, ncp` |

exponential | `exp` |
`rate` |

F | `f` |
`df1` , `df2, ncp` |

gamma | `gamma` |
`shape` , `rate` |

geometric | `geom` |
`p` |

hypergeometric | `hyper` |
`m` , `n` , `k` |

log-normal | `lnorm` |
`meanlog` , `sdlog` |

logistic | `logis` |
`location` , `scale` |

negative binomial | `nbinom` |
`size` , `prob` |

normal | `norm` |
`mean` , `sd` |

Poisson | `pois` |
`lambda` |

Student t | `t` |
`df, ncp` |

uniform | `unif` |
`min` , `max` |

Weibull | `weibull` |
`shape` , `scale` |

**Updated **Aug 20: added the ncp parameter to beta, chisq, f, and t with thanks to Doug Bates’ comment below.

John D Cook: Distributions in R and S-PLUS

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