- 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.

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 distributionâ€™s domain via truncation.