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CRAN v0.1.0
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@mitchelloharawild mitchelloharawild released this 09 Jun 13:53
v0.1.0
001d843

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.
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