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bgms is now the default fitting package for every data
type, including type = "continuous" and
type = "mixed", which previously defaulted to BGGM. Pass
package = "BGGM" or package = "BDgraph" to
keep the old behaviour.type may now be given as a per-variable character
vector, for example
type = c("ordinal", "ordinal", "continuous").cauchy_prior(), normal_prior(),
beta_prime_prior(), bernoulli_prior(),
beta_bernoulli_prior(), sbm_prior()). The
older flat arguments still work and are translated internally.blume_capel_parameters, a data frame holding the posterior
mean, posterior standard deviation, 95% credible interval and R-hat of
the linear and quadratic effect of each Blume-Capel variable, together
with the baseline category it was fitted with. Unlike the category
thresholds of an ordinal variable, these two parameters are usually of
substantive interest, so they are also printed by summary()
rather than left in the fit object.save = TRUE, the posterior draws of those effects
are returned in samples_blume_capel.For Blume-Capel variables, the two columns of
thresholds were labelled cat (1) and
cat (2), the same headers bgms uses for genuine category
thresholds. They are in fact the linear and quadratic effect, and are
now named accordingly. Where Blume-Capel and ordinal variables share one
matrix the headers cannot describe both, so the per-row meaning is
recorded in the matrix’s "variable_type"
attribute.
print() on an unsummarised easybgm
object printed the closing notes twice, once from the summary it prints
internally and once from its own tail.
centrality for bgms fits was computed from a
mis-permuted edge matrix: the posterior samples were read back in BGGM’s
upper-triangle order rather than the lower-triangle order bgms uses.
Per-node strengths were therefore permuted, and
plot_centrality() reported them under the wrong node
labels. The ordering is now stated explicitly at each call
site.
structure was returned as a complete graph (a matrix
of ones) whenever save = FALSE, which is the default.
plot_structure() consequently drew a fully connected
network. It is now the median probability model in both branches,
matching the documentation.
The Monte Carlo interval in MCSE_BF mixed two
estimators: it took the binomial variance of the raw indicator average
but divided it by the effective sample size of the Rao-Blackwellized
chain, and attached the result to a Rao-Blackwellized Bayes factor. The
interval was too wide by up to about 40%. It is now computed from the
Monte Carlo standard error that bgms reports for the Rao-Blackwellized
inclusion probability.
plot_centrality() and
plot_prior_sensitivity() failed on lists of raw bgms fit
objects, because bgms no longer reports save among the fit
arguments. Both now work, and both record the model type
correctly.
clusterBayesfactor() failed on a raw bgms fit object
with “invalid to use names()<- on an S4 object”. It now reads the
prior and the block posterior through the bgms extractor functions, and
gives an informative error when the fit was not estimated with the
Stochastic Block Model prior.
The legacy interaction_scale argument no longer
leaks a bgms deprecation warning; it is translated to
cauchy_prior(scale) like
pairwise_scale.
Corrected the documented defaults for
interaction_prior and precision_scale_prior,
and documented precision_graph_prior and
difference_family.
Fitting with bgms >= 0.2.0.0 changes several numbers relative to easybgm 0.4.0 with bgms 0.1.6.3. None of these is a bug in either package:
2 * omega * x,
where 0.1.6.3 stored 2 * omega. This affects
parameters, samples_posterior,
centrality, and every plot drawn from them.partial_correlations and
precision_matrix, and summary() now states
which scale the reported edge weights are on.0 or Inf, and an edge can cross the
median-probability threshold differently than before.normal_prior(scale = 1), on the new coordinate. A Normal
slab has much lighter tails than a Cauchy and constrains weakly
identified edges more tightly.convergence_parameter is the classic
split-R-hat. bgms 0.1.6.3 applied a degrees-of-freedom
adjustment that reported about 1.29 on nearly saturated indicators, that
is, on the most decisive edges. Those now report near 1. NA
and Inf are possible when all chains are identical or
stuck.DESCRIPTION. The fixes above apply to both bgms versions,
and the test suite now exercises them on 0.1.6.3 as well as on 0.2.0.0
rather than skipping them.warmup explicitly. bgms
defaults to warmup = 2000 regardless of iter,
which dominated the runtime of the examples. warmup is a
bgm() argument in both supported bgms versions.vdiffr dependency and the
LazyData field, and dropped some dead version-gating
code.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.