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.