Probability Distributions as S3 Objects
OTHER License
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
message = FALSE,
warning = FALSE
)
distributions3
, inspired by the eponynmous Julia package, provides a generic function interface to probability distributions. distributions3
has two goals:
Replace the rnorm()
, pnorm()
, etc, family of functions with S3 methods for distribution objects
Be extremely well documented and friendly for students in intro stat classes.
The main generics are:
random()
: Draw samples from a distribution.pdf()
: Evaluate the probability density (or mass) at a point.cdf()
: Evaluate the cumulative probability up to a point.quantile()
: Determine the quantile for a given probability. Inverse of cdf()
.You can install distributions3
with:
install.packages("distributions3")
You can install the development version with:
install.packages("devtools")
devtools::install_github("alexpghayes/distributions3")
The basic usage of distributions3
looks like:
library("distributions3")
X <- Bernoulli(0.1)
random(X, 10)
pdf(X, 1)
cdf(X, 0)
quantile(X, 0.5)
Note that quantile()
always returns lower tail probabilities. If you aren't sure what this means, please read the last several paragraphs of vignette("one-sample-z-confidence-interval")
and have a gander at the plot.
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 vectorized S3 objectsdistr6
builds on distr
, but uses R6 objectsdistr
is quite similar to distributions
, but 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