Added a symbol argument to all plot.*()
methods, allowing study-level markers to be drawn as rectangles,
ellipses, diamonds, triangles, crosses, plus signs, or stars.
Subgroup plots now distinguish subgroups by point symbol as well
as colour by default. The previous appearance, using rectangles for all
subgroups, can be reproduced by setting
symbol = "rectangle".
Added the size = "sampsize_revman" and
size = "se_revman" options, following RevMan-style
symbol-sizing conventions, to support visual consistency with
as_revman() exports. The analogous
size = "eb_revman" option is available for latent-class
models.
Corrected a bug in plot.Reitsma() with the default
size = "fisher". The covariance used to construct the
matrix G for the SROC plot is now calculated
correctly.
Added the size = "fisher" option to
plot.RutterGatsonis(),
plot.RutterGatsonisSubgroup(), and
plot.ReitsmaSubgroup(). It is now the default in
plot.RutterGatsonis().
Added fitReitsmaTMB() and
fitReitsmaSubgroupTMB(), which use custom TMB templates
rather than glmmTMB and may provide improved numerical
robustness in difficult or near-boundary cases.
Corrected a bug in the plot() and
as_revman() methods for ReitsmaSubgroup models
when sensitivity and/or specificity are constrained to be common across
subgroups.
Added optional study-level confidence intervals
(studyCI = TRUE) and study labels
(studylabels = TRUE) to SROC plotting methods.
Improved starting-value initialization across model families. Robust, finite fallback values are now used when preliminary model fits or empirical estimates are unavailable, including for sparse and degenerate datasets.
Revised the likelihood-ratio-test examples and documentation to caution against using the ordinary chi-squared reference distribution when a reduced model constrains one or more random-effects variances to zero. The corresponding zero-variance boundary comparison was removed from the vignette.
Fixed manual-page titles to use title case consistently.
summary() methods now return objects of dedicated S3
classes ("summary.DTAmodel" and model-specific subclasses)
rather than plain lists, with a corresponding
print.summary.DTAmodel() method.
Refactored forest-plot construction across model classes into
shared internal helpers (getForestSensSpec(),
getForestSensSpecLCA(), and getForestPlot())
to reduce code duplication.
coef() and vcov() methods are now
available for all model classes. Examples were updated to use
summary() and these accessors rather than accessing
internal list components directly.