CRAN v0.5.0

New features

• support() now shows whether the interval of support is open or
  closed (@venpopov, #97).
• Added default method for cdf() which estimates the CDF using Monte Carlo
  integration (@robjhyndman, #122).

Probability distributions

• Added dist_gk() for g-and-k distributions.
• Added dist_gh() for g-and-h distributions.
• Added dist_gev() for the Generalised Extreme Value distribution and
  dist_gpd() for the Generalised Pareto distribution (@robjhyndman, #124).

Improvements

• dist_mixture() now displays the components of the mixture when the output
  width is sufficiently wide (@statasaurus, #112).
• generate() now respects dimnames() for multivariate distributions.
• dist_mixture() now supports multivariate distributions (@robjhyndman, #122).

Bug fixes

• Fixed error when using '-' as a unary operator on a distribution different from
  dist_normal() (@venpopov, #95)
• Density for transformed distributions now correctly gives 0 instead of NaNs for
  values outside the support of the distribution (@venpopov, #97)
• Fixed quantile() and cdf() for transformed distributions with
  monotonically decreasing transformations (#100).
• Fixed multivariate dist_sample() methods not structuring multivariate
  results correctly as matrices.
• The cdf() method for dist_multivariate_normal() now gives the P(X <= q)
  rather than P(X > q) for consistency with all other cdf() methods.
• The quantile() method for dist_multivariate_normal() now correctly gives
  the boundaries when p=0 or p=1 when type="equicoordinate".
• The quantile() method for dist_multivariate_normal() now only square roots
  the diagonal elements when type="marginal".

CRAN v0.3.2

Small patch to resolve issues with CRAN checks.

Bug fixes

• 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().

CRAN v0.4.0

Breaking changes

• 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.

CRAN v0.3.1

New features

• 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().

Improvements

• 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}.

Bug fixes

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

CRAN v0.3.0

New features

Probability distributions

• 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().

Generics

• 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.

Improvements

• 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.

Bug fixes

• Fixed fallback format() function for distributions classes that have not
  defined this method (#67).

Breaking changes

• 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().

CRAN v0.2.1

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.

New features

Probability distributions

• Added dist_missing() for representing unknown or missing (NA) distributions.

Improvements

• 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.

Bug fixes

• 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).

CRAN v0.2.0

New features

Probability distributions

• Added dist_wrap() for wrapping distributions not yet added in the package.

Methods

• 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.

Improvements

• 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.

Deprecated features

• 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.

CRAN v0.1.0

First release.

New features

Object classes

• 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.

Generic functions

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).

Graphics

• 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.

Probability distributions

• 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()

Distribution modifiers

• 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.