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.
max/cdf_cutoff attributes) and raise an
informative error if it is malformed.Beta() distribution
(shape1/shape2, or
mean/sd).Exponential() and Weibull()
distributions.Dirichlet() and support for uncertain
nonparametric distributions specified via a Dirichlet prior
(NonParametric(pmf = Dirichlet(...))).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.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.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).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.dist.spec to
distspec.<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.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).Exp() is deprecated in favour of
Exponential().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.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.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.== (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.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.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().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.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.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.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.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.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.
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