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")
::install_github("alexpghayes/distributions3") devtools
The basic usage of distributions3
looks like:
library("distributions3")
<- Bernoulli(0.1)
X
random(X, 10)
#> [1] 0 0 0 0 0 0 1 1 0 0
pdf(X, 1)
#> [1] 0.1
cdf(X, 0)
#> [1] 0.9
quantile(X, 0.5)
#> [1] 0
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