R package for Bayesian meta-analysis models, using Stan
Binaries are available on CRAN
New functionality:
baggr_plot
can be made to look more like forest plot with baggr_plot(bg, style = "forest")
add_values
functionality.baggr_plot(bg, hyper = "red")
bubble()
Back end and minor changes:
hypermean()
and hypersd()
(defaults to summaries)loo_compare
now has better annotationBugfixes:
pooling_control = "remove"
when calling baggr()
. This will avoid estimating parameters which are known to be 0.effect_draw(object, newdata = ...)
or (equivalently) predict(object, newdata = ...)
to generate predictions for any number of new samplesMisc:
baggr()
without any extra steps like prepare_ma()
, by just defining effect
when running baggr (or it will default to log OR).posterior_predict()
for drawing from posterior
sample. This is more consistent with regression modeling and RStan ecosystem.Bugs:
baggr_compare
plots previously didn't work for some plots. This is now fixed.Misc:
baggr
and baggr_compare
objects is now better at showing intervals and you can also change their widths with arguments passed to print.baggr()
or directly to baggr_compare()
student_t()
and lognormal()
priors and updated some prior documentationBinaries are available on CRAN
summary
option for effect_draw
.New "mutau_full"
model is a generalisation of the "mutau"
model into individual-level data.
The idea is similar as for the recent "rubin_full"
changes, see version 0.6.0.
I also reparameterised the mutau
model. It should be faster and have fewer divergent
transition warnings.Some of the code around the mu and tau model has also been
rewritten on the back end.
On the back end the package now follows the rstantools recommended way of compiling models. The user experience should be exactly the same, but this may avoid some problems when installing the package from GitHub or otherwise compiling it locally.
model="sslab"
. See ?baggr
for basics of
working with this type of a model. A vignette will be added soon.model="rubin_full"
rather than "full"
.
Old syntax will still work, however. Made some documentation and code improvements
around this issue.model="rubin_full"
now. It works the same
way as for model="logit"
. See ?baggr
for more information on how to use it.model="rubin"
with the same inputs as model="mutau"
.
Some data columns are removed automatically in that case.For v0.6 we added more generic code around plotting, printing, grabbing treatment effects etc. While there are no differences on the front-end, this means that for the next versions we will be able to consider some new models and have more homogeneous syntax for all models.
rubin_full
(full
) model.baggr_compare
plots.baggr
models now have their own separate functions,
fixed_effects
and random_effects
, in addition to group_effects
labbe()
.baggr()
with summary data and model="logit"
for automatic conversion)prior_control
and prior_control_sd
in baggr()
Binaries are available at CRAN
show = "covariates"
baggr()
using a syntax similar to rstanarm
.
Extra priors are availablebaggr()
outputs prior predictive distributions; they can be examined using
baggr_compare
and effect_plot
, effect_draw
-- 2 new functionsAvailable at https://CRAN.R-project.org/package=baggr