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distributions3: Probability Distributions as S3 Objects

distributions3 provides a comprehensive and object-oriented toolbox for probability distributions.

Features:

  • Distributions are provided as S3 objects. Essentially, these are represented as data frames of parameters but with a dedicated class, e.g., "Normal", inheriting from "distribution".

  • Methods pdf() (probability density function or probability mass function), cdf() (cumulative distribution function), quantile() (quantile function, inverse of cdf()), and random() (random samples) replace the d/p/q/r functions provided in base R and many other packages such as dnorm(), pnorm(), qnorm(), rnorm() for the normal distribution.

  • Methods for computing central moments via mean(), variance(), skewness(), and kurtosis().

  • Methods for extracting the score() (first derivative of the log-likelihood with respect to the parameters) and hessian() (corresponding second derivative.

  • Numerical fallback implementations for most of these methods in case no dedicated method is available for a certain distribution.

Goals:

  • User-friendly interface for illustrations in introductory statistics classes and also advanced probabilistic modeling and regression courses.

  • Developer-friendly interface for fitting and assessing probabilistic models, e.g., via gamlss2 and topmodels.

Installation

The stable version of distributions3 is available from CRAN:

install.packages("distributions3")

The latest development version can be installed from R-universe:

install.packages("distributions3", repos = "https://zeileis.R-universe.dev")

Basic usage

The basic usage of distributions3 looks like:

library("distributions3")

## Normal "IQ" distribution
X <- Normal(100, 15)
X
#> [1] "Normal(mu = 100, sigma = 15)"

random(X, 5)
#> [1] 118.94  95.11 119.95 119.09 106.22

pdf(X, 85)
#> [1] 0.01613
cdf(X, 85)
#> [1] 0.1587
quantile(X, 0.159)
#> [1] 85.02

mean(X)
#> [1] 100
variance(X)
#> [1] 225
skewness(X)
#> [1] 0

## Vector of Poisson "soccer goals" distributions
Y <- Poisson(c(0.8, 1.2, 1.9, 0.5))
Y
#> [1] "Poisson(lambda = 0.8)" "Poisson(lambda = 1.2)" "Poisson(lambda = 1.9)"
#> [4] "Poisson(lambda = 0.5)"

random(Y, 5)
#>      r_1 r_2 r_3 r_4 r_5
#> [1,]   0   0   4   0   0
#> [2,]   0   2   1   1   0
#> [3,]   1   2   3   0   1
#> [4,]   1   1   2   0   1

pdf(Y, 0:5)
#>         d_0    d_1     d_2     d_3      d_4      d_5
#> [1,] 0.4493 0.3595 0.14379 0.03834 0.007669 0.001227
#> [2,] 0.3012 0.3614 0.21686 0.08674 0.026023 0.006246
#> [3,] 0.1496 0.2842 0.26997 0.17098 0.081216 0.030862
#> [4,] 0.6065 0.3033 0.07582 0.01264 0.001580 0.000158
cdf(Y, 0:5)
#>         p_0    p_1    p_2    p_3    p_4    p_5
#> [1,] 0.4493 0.8088 0.9526 0.9909 0.9986 0.9998
#> [2,] 0.3012 0.6626 0.8795 0.9662 0.9923 0.9985
#> [3,] 0.1496 0.4337 0.7037 0.8747 0.9559 0.9868
#> [4,] 0.6065 0.9098 0.9856 0.9982 0.9998 1.0000
quantile(Y, 0.5)
#> [1] 1 1 2 0

mean(Y)
#> [1] 0.8 1.2 1.9 0.5
variance(Y)
#> [1] 0.8 1.2 1.9 0.5
skewness(Y)
#> [1] 1.1180 0.9129 0.7255 1.4142

Contributing

If you are interested in contributing to distributions3, please reach out on Github! We are happy to review PRs contributing bug fixes.

Please note that distributions3 is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

For a comprehensive overview of the many packages providing various distribution related functionality see the CRAN Task View.

  • distributional provides distribution objects as S3 vctr objects.
  • distr6 builds on distr, but uses R6 objects
  • distr is similar in spirit to distributions3, but is not vectorized, uses S4 objects, and is less focused on documentation.
  • fitdistrplus provides extensive functionality for fitting various distributions but does not treat distributions themselves as objects