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distspec 0.1.0

distspec 0.1.0 splits the <dist_spec> interface out of EpiNow2. The entries below are changes relative to that code as it stood in EpiNow2 1.9.0.

New features

  • Distribution constructors now validate the structure of the object they build (class, parameters, and max/cdf_cutoff attributes) and raise an informative error if it is malformed.
  • Added a Beta() distribution (shape1/shape2, or mean/sd).
  • Added Exponential() and Weibull() distributions.
  • Added Dirichlet() and support for uncertain nonparametric distributions specified via a Dirichlet prior (NonParametric(pmf = Dirichlet(...))).
  • Added sample_dist() to draw random samples from a distribution with fixed parameters. A composite distribution is sampled per component, returning an n by k matrix (rowSums() gives samples of the combined distribution). Distributions with uncertain (prior) parameters cannot be sampled and raise an error.
  • Added has_uncertainty(), a predicate for whether a <dist_spec> (or a component of a composite) carries a prior, so dependent packages and internal code can test for uncertainty in one place.
  • Uncertainty in a distribution specified with non-natural parameters (e.g. Gamma(mean = Normal(4, 0.5), sd = 1)) is now propagated to the natural parameters with a first-order delta-method approximation. This replaces an ad-hoc rule that understated the natural-parameter standard deviations several times over.
  • discretise() gains a remove_trailing_zeros argument (default TRUE).
  • Exported the lower-level helpers sd(), ndist(), natural_params() and lower_bounds() so that dependent packages can reuse them.
  • natural_params() and lower_bounds() accept a distribution type given by name (e.g. natural_params("gamma")), as well as a <dist_spec>, so dependent packages can query type metadata without constructing an instance.

Breaking changes

  • The package has been renamed from dist.spec to distspec.
  • A distribution's type is now carried in the S3 class of its <dist_spec> (e.g. c("gamma", "dist_spec")), so per-type behaviour dispatches directly and each distribution's methods live in one place. The internal distribution dispatch class and new_dist() have been removed. The internal helpers natural_params() and lower_bounds() now take a <dist_spec> rather than a distribution-name string.
  • get_parameters() is now an S3 generic.
  • The cdf_cutoff argument (on the distribution constructors and bound_dist()) is the cumulative probability to keep up to: cdf_cutoff = 0.999 truncates at the 99.9th percentile, and the default 1 keeps the full distribution. A value below 0.5 is rejected, as it is almost certainly the tail probability to drop (use 1 - x).

Deprecations

  • Exp() is deprecated in favour of Exponential().

Bug fixes

  • NonParametric() and Dirichlet(prior = ) now reject a numeric PMF or weight vector that contains negative or non-finite values, or is all zero, with an informative error, instead of silently producing an invalid distribution. Un-normalised non-negative weights are still accepted and normalised, but now warn when they do not sum to one.
  • A distribution parameter given as a certain distribution (standard deviation 0, e.g. Normal(x, 0), which collapses to Fixed(x)) is now resolved to its point value at construction, so it behaves exactly like passing the number. Previously such a parameter left the distribution marked uncertain, so mean() and sd() returned NA for an otherwise fully-fixed distribution (e.g. Gamma(shape = Normal(3, 0), rate = 2)).
  • sd() of a nonparametric distribution now returns the standard deviation rather than the variance (a missing square root). This also affects sd() of any discretised distribution, since discretise() produces a nonparametric distribution.
  • collapse() now correctly convolves runs of three or more consecutive nonparametric distributions, and runs that do not begin at the first component, rather than erroring or convolving the wrong component.
  • Convolution in collapse() now uses a numerically stable implementation.
  • bound_dist() now truncates a fixed nonparametric PMF at max when the PMF is longer than max + 1, renormalising the result, and leaves it untouched when max reaches beyond the support. Previously the condition was inverted, so the bound never applied when requested and produced an all-NA PMF when max exceeded the support.
  • Comparing two distributions with == (or !=) no longer errors when a parameter is a numeric vector of length greater than one; such parameters are now compared as whole vectors.
  • fix_parameters() and discretise() now forward strategy and remove_trailing_zeros to the components of a composite distribution, so these arguments are no longer silently ignored for composites.
  • Fixed() distributions may now take a value of 0; the lower bound for the value parameter has been corrected accordingly, and a value below that bound is now rejected with an informative error instead of silently producing an invalid probability mass function.
  • An uncertain (Dirichlet-backed) nonparametric distribution is now treated consistently as uncertain, storing its Dirichlet prior in place of a concrete PMF just as an uncertain parametric distribution stores a dist_spec for a parameter. It has no PMF until resolved with fix_parameters(): get_pmf() errors on such a distribution, mean() returns NA (or the prior mean with ignore_uncertainty = TRUE), and it prints with its prior nested like any other uncertain distribution.
  • Applying max or cdf_cutoff to an uncertain (Dirichlet-backed) nonparametric distribution now raises an informative error, since its support is fixed by the Dirichlet prior and the bound would otherwise be silently ignored.
  • plot() gives an actionable error when asked to plot a distribution with no finite range (no finite max and no cdf_cutoff), pointing to bound_dist(), rather than a cryptic message or a silently chosen default range.
  • mean() and sd() now emit an informative message when they return NA because a distribution has uncertain parameters, pointing to mean(x, ignore_uncertainty = TRUE) and fix_parameters().
  • Improved the error messages from get_element() and get_parameters(): an out-of-range id now reports the offending value and valid range, and the nonparametric error no longer implies that Weibull, Beta and Exponential distributions lack parameters.

Documentation

  • Each distribution now has its own reference page (Gamma(), LogNormal(), ...) rather than a single combined page, so each shows only its own parameters. The reference index covers the full exported API, and the discretise() help page documents how discretisation works, including the fixed point-mass special case.
  • Documentation improvements: the getting-started vignette now shows the end-to-end get_pmf(collapse(discretise(d1 + d2))) pipeline for combining two delays into a single PMF, stale EpiNow2 and Stan references have been removed from the roxygen, and the bound_dist(), discretise(), fix_parameters() and sd() help pages have clearer descriptions and runnable examples.

Package changes

  • Discretisation now uses the primarycensored package to compute double censored probability mass functions.
  • natural_params() and lower_bounds() are now S3 generics, with each distribution's behaviour defined alongside its type (in its own R/ file) rather than in scattered switch()/if statements. Gamma(), Normal(), LogNormal(), Exp(), Weibull(), Beta(), Fixed(), the Dirichlet() prior and the nonparametric distribution now define their per-type behaviour (parameter metadata, and mean()/sd()/max() where applicable) this way. The internal per-distribution switch() statements have been collapsed to direct S3 dispatch; attempting to discretise a distribution that has no CDF now reports this directly.
  • Internal idiomatic cleanups: switched the uncertain-parameter checks to the existing has_uncertainty() predicate (removing a near-duplicate helper), threaded pre-computed parameter means through to_natural() to avoid redundant lapply(x$parameters, mean) calls in every method, vectorised the attribute-copy loop in discretise(), used %||% for null-default attribute guards, and extracted repeated get_parameters() calls and sum(convolutions) into local variables.
  • Reduced dependencies: dropped data.table, checkmate and purrr, and moved ggplot2 to Suggests. plot() now prompts to install ggplot2 if it is missing, so it is no longer a hard dependency of the package.