Distributions in R

August 18, 2010

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

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`