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.
dist_missing()for representing unknown or missing (NA) distributions.
dist_sample()which uses the emperical cdf.
dimnames()if all distributions have the same
generate()methods for sample distributions.
dist_truncated()distributions with no upper or lower limit.
dist_wrap()for wrapping distributions not yet added in the package.
likelihood()for computing the likelihood of observing a sample from a distribution.
skewness()for computing the skewness of a distribution.
kurtosis()for computing the kurtosis of a distribution.
quantile()methods now accept a
logargument which will use/return probabilities as log probabilities.
hilo()intervals can no longer be added to other intervals, as this is a common mistake when aggregating forecasts.
numDeriv::hessian()when computing mean and variance of transformed distributions.
autoplot.distribution()is now deprecated in favour of using the
ggdistpackage allows distributions produced by distributional to be used directly with ggplot2 as aesthetics.
distribution: Distributions are represented in a vectorised format using the vctrs package. This makes distributions suitable for inclusion in model prediction output. A
distributionis a container for distribution-specific S3 classes.
hilo: Intervals are also stored in a vector. A
hiloconsists of a
upperbound, 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
hilovalues. 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
cdf(): The cumulative distribution function (equivalent to
generate(): Random generation from the distribution (equivalent to
quantile(): Compute quantiles of the distribution (equivalent to
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).
autoplot()method for visualising the probability density function ([
density()]) or cumulative distribution function ([
cdf()]) of one or more distribution.
geom_hilo_linerange()geometries for ggplot2. These geoms allow uncertainty to be shown graphically with
dist_inflated()which inflates a specific value of a distribution by a given probability. This can be used to produce zero-inflated distributions.
dist_transformed()for transforming distributions. This can be used to produce log distributions such as logNormal:
dist_transformed(dist_normal(), transform = exp, inverse = log)
dist_mixture()for producing weighted mixtures of distributions.
dist_truncated()to impose boundaries on a distribution’s domain via truncation.