support() now shows whether the interval of support is
open or closed (@venpopov, #97).cdf() which estimates the CDF
using Monte Carlo integration (@robjhyndman, #122).dist_gk() for g-and-k distributions.dist_gh() for g-and-h distributions.dist_gev() for the Generalised Extreme Value
distribution and dist_gpd() for the Generalised Pareto
distribution (@robjhyndman, #124).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).dist_normal() (@venpopov, #95)quantile() and cdf() for transformed
distributions with monotonically decreasing transformations (#100).dist_sample() methods not
structuring multivariate results correctly as matrices.cdf() method for
dist_multivariate_normal() now gives the P(X <= q)
rather than P(X > q) for consistency with all other
cdf() methods.quantile() method for
dist_multivariate_normal() now correctly gives the
boundaries when p=0 or p=1 when
type="equicoordinate".quantile() method for
dist_multivariate_normal() now only square roots the
diagonal elements when type="marginal".Small patch to resolve issues with CRAN checks.
quantile(<dist_mixture>).cdf(<dist_sample>) as Pr(X <= x), not
Pr(X < x).p for
log_quantile().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).scale argument for alternative specification for
dist_burr() and dist_gamma().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.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.dist_categorical() for the Categorical
distribution.dist_lognormal() for the log-normal distribution.
Mathematical conversion shortcuts have also been added, so
exp(dist_normal()) produces
dist_lognormal().parameters() generic for obtaining the
distribution’s parameters.family(<distribution>) for getting the
distribution’s family name.covariance() to return the covariance of a
distribution.support() to identify the distribution’s region
of support (#8).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).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.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().is_distribution() to identify if an object is a
distribution.is.na() and vctrs vector resizing / filling
functionality.as.character(<hilo>) method, allowing
datasets containing hilo() objects to be saved as a text
file (#57).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.
dist_missing() for representing unknown or
missing (NA) distributions.cdf() method for dist_sample() which
uses the emperical cdf.dist_mixture() now preserves dimnames() if
all distributions have the same dimnames().density() and generate() methods for
sample distributions.skewness() method for
dist_sample().dist_truncated() 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.density(), cdf() and
quantile() methods now accept a log argument
which will use/return probabilities as log probabilities.distributions3 package.View().hilo() intervals can no longer be added to other
intervals, as this is a common mistake when aggregating forecasts.d for numDeriv::hessian() when
computing mean and variance of transformed distributions.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).autoplot() method for visualising the
probability density function ([density()]) or cumulative
distribution function ([cdf()]) of one or more
distribution.geom_hilo_ribbon() and
geom_hilo_linerange() geometries for ggplot2. These geoms
allow uncertainty to be shown graphically with hilo()
intervals.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()dist_bernoulli(), dist_binomial(),
dist_geometric(), dist_logarithmic(),
dist_multinomial(), dist_negative_binomial(),
dist_poisson(),
dist_poisson_inverse_gaussian()dist_degenerate(), dist_percentile(),
dist_sample()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.