- All graphics related functionality has been removed from the package in favour of the ggdist (https://cran.r-project.org/package=ggdist) package. This breaking change was done to substantially reduce the package’s dependencies, focusing the functionality on representing vectors of distributions.

Small patch to resolve issues with CRAN checks.

- Fixed object structure resulting from transforming sample distributions (#81).
- Improved reliability of
`quantile(<dist_mixture>)`

. - Defined
`cdf(<dist_sample>)`

as Pr(X <= x), not Pr(X < x). - Fixed S3 generic argument name
`p`

for`log_quantile()`

.

- Add Math and Ops methods for sample distribution, which applies the functions directly to the samples.
- Added
`mean`

and`sd`

as aliases for`mu`

and`sigma`

respectively in`dist_normal()`

and`dist_student_t()`

to match arguments of the stats package interface (#76). - Added
`scale`

argument for alternative specification for`dist_burr()`

and`dist_gamma()`

.

- Generics introduced by this package now allow
`na.rm`

and other parameters to be passed to distribution methods, even if these parameters aren’t used. The package no longer checks the usage of`...`

with the`ellipsis`

package, if you’d like to check that all`...`

are used, you can write your own wrapping functions. - Lists of functions can now be used in
`dist_transformed()`

, allowing the transformation to differ for each distribution. `covariance()`

and other matrix output functions of multivariate distributions now name the result using the distribution’s dimension names.- Improve handling of mixture distribution quantiles at boundaries {0,1}.

- Fixed issue with computing multiple values from a univariate distribution with named dimensions (#79).

- Added
`dist_categorical()`

for the Categorical distribution. - Added
`dist_lognormal()`

for the log-normal distribution. Mathematical conversion shortcuts have also been added, so`exp(dist_normal())`

produces`dist_lognormal()`

.

- Added
`parameters()`

generic for obtaining the distribution’s parameters. - Added
`family(<distribution>)`

for getting the distribution’s family name. - Added
`covariance()`

to return the covariance of a distribution. - Added
`support()`

to identify the distribution’s region of support (#8). - Added
`log_likelihood()`

for computing the log-likelihood of observing a sample from a distribution.

`variance()`

now always returns a variance. It will not default to providing a covariance matrix for matrices. This also applies to multivariate distributions such as`dist_multivariate_normal()`

. The covariance can now be obtained using the`covariance()`

function.`dist_wrap()`

can now search for distribution functions in any environment, not just packages. If the`package`

argument is`NULL`

, it will search the calling environment for the functions. You can also provide a package name as before, and additionally an arbitrary environment to this argument.`median()`

methods will now ignore the`na.rm`

option when it does not apply to that distribution type (#72).`dist_sample()`

now allows for missing values to be stored. Note that`density()`

,`quantile()`

and`cdf()`

will remove these missing values by default. This behaviour can be changed with the`na.rm`

argument.`<hilo>`

objects now support non-numeric and multivariate distributions.`<hilo>`

vectors that have different bound types cannot be mixed (#74).- Improved performance of default methods of
`mean()`

and`variance()`

, which no longer use sampling based means and variances for univariate continuous distributions (#71, @mjskay) `dist_binomial()`

distributions now return integers for`quantile()`

and`generate()`

methods.- Added conditional examples for distributions using functions from supported packages.

- Fixed fallback
`format()`

function for distributions classes that have not defined this method (#67).

`variance()`

on a`dist_multivariate_normal()`

will now return the diagonal instead of the complete variance-covariance matrix.`dist_bernoulli()`

will now return logical values for`quantile()`

and`generate()`

.

- Added
`is_distribution()`

to identify if an object is a distribution.

- Improved NA structure of distributions, allowing it to work with
`is.na()`

and`vctrs`

vector resizing / filling functionality. - Added
`as.character(<hilo>)`

method, allowing datasets containing`hilo()`

objects to be saved as a text file (#57).

- Fixed issue with
`hdr()`

range`size`

incorrectly being treated as`100-size`

, giving 5% ranges for 95% sizes and vice-versa (#61).

A small performance and methods release. Some issues with truncated distributions have been fixed, and some more distribution methods have been added which improve performance of common tasks.

- Added
`dist_missing()`

for representing unknown or missing (NA) distributions.

- Documentation improvements.
- Added
`cdf()`

method for`dist_sample()`

which uses the emperical cdf. `dist_mixture()`

now preserves`dimnames()`

if all distributions have the same`dimnames()`

.- Added
`density()`

and`generate()`

methods for sample distributions. - Added
`skewness()`

method for`dist_sample()`

. - Improved performance for truncated Normal and sample distributions (#49).
- Improved vectorisation of distribution methods.

- Fixed issue with computing the median of
`dist_truncated()`

distributions. - Fixed format method for
`dist_truncated()`

distributions with no upper or lower limit. - Fixed issue with naming
objects giving an invalid structure. It now gives an informative error (#23). - Fixed documentation for Negative Binomial distribution (#46).

- Added
`dist_wrap()`

for wrapping distributions not yet added in the package.

- Added
`likelihood()`

for computing the likelihood of observing a sample from a distribution. - Added
`skewness()`

for computing the skewness of a distribution. - Added
`kurtosis()`

for computing the kurtosis of a distribution. - The
`density()`

,`cdf()`

and`quantile()`

methods now accept a`log`

argument which will use/return probabilities as log probabilities.

- Improved documentation for most distributions to include equations for the region of support, summary statistics, density functions and moments. This is the work of @alexpghayes in the
`distributions3`

package. - Documentation improvements
- Added support for displaying distributions with
`View()`

. `hilo()`

intervals can no longer be added to other intervals, as this is a common mistake when aggregating forecasts.- Incremented
`d`

for`numDeriv::hessian()`

when computing mean and variance of transformed distributions.

- Graphics functionality provided by
`autoplot.distribution()`

is now deprecated in favour of using the`ggdist`

package. The`ggdist`

package allows distributions produced by distributional to be used directly with ggplot2 as aesthetics.

First release.

`distribution`

: Distributions are represented in a vectorised format using the vctrs package. This makes distributions suitable for inclusion in model prediction output. A`distribution`

is a container for distribution-specific S3 classes.`hilo`

: Intervals are also stored in a vector. A`hilo`

consists of a`lower`

bound,`upper`

bound, and confidence`level`

. Each numerical element can be extracted using`$`

, for example my_hilo$lower to obtain the lower bounds.`hdr`

: Highest density regions are currently stored as lists of`hilo`

values. This is an experimental feature, and is likely to be expanded upon in an upcoming release.

Values of interest can be computed from the distribution using generic functions. The first release provides 9 functions for interacting with distributions:

`density()`

: The probability density/mass function (equivalent to`d...()`

).`cdf()`

: The cumulative distribution function (equivalent to`p...()`

).`generate()`

: Random generation from the distribution (equivalent to`r...()`

).`quantile()`

: Compute quantiles of the distribution (equivalent to`q...()`

).`hilo()`

: Compute probability intervals of probability distribution(s).`hdr()`

: Compute highest density regions of probability distribution(s).`mean()`

: Obtain the mean(s) of probability distribution(s).`median()`

: Obtain the median(s) of probability distribution(s).`variance()`

: Obtain the variance(s) of probability distribution(s).

- Added an
`autoplot()`

method for visualising the probability density function ([`density()`

]) or cumulative distribution function ([`cdf()`

]) of one or more distribution. - Added
`geom_hilo_ribbon()`

and`geom_hilo_linerange()`

geometries for ggplot2. These geoms allow uncertainty to be shown graphically with`hilo()`

intervals.

- Added 20 continuous probability distributions:
`dist_beta()`

,`dist_burr()`

,`dist_cauchy()`

,`dist_chisq()`

,`dist_exponential()`

,`dist_f()`

,`dist_gamma()`

,`dist_gumbel()`

,`dist_hypergeometric()`

,`dist_inverse_exponential()`

,`dist_inverse_gamma()`

,`dist_inverse_gaussian()`

,`dist_logistic()`

,`dist_multivariate_normal()`

,`dist_normal()`

,`dist_pareto()`

,`dist_student_t()`

,`dist_studentized_range()`

,`dist_uniform()`

,`dist_weibull()`

- Added 8 discrete probability distributions:
`dist_bernoulli()`

,`dist_binomial()`

,`dist_geometric()`

,`dist_logarithmic()`

,`dist_multinomial()`

,`dist_negative_binomial()`

,`dist_poisson()`

,`dist_poisson_inverse_gaussian()`

- Added 3 miscellaneous probability distributions:
`dist_degenerate()`

,`dist_percentile()`

,`dist_sample()`

- Added
`dist_inflated()`

which inflates a specific value of a distribution by a given probability. This can be used to produce zero-inflated distributions. - Added
`dist_transformed()`

for transforming distributions. This can be used to produce log distributions such as logNormal:`dist_transformed(dist_normal(), transform = exp, inverse = log)`

- Added
`dist_mixture()`

for producing weighted mixtures of distributions. - Added
`dist_truncated()`

to impose boundaries on a distribution’s domain via truncation.