version 3.1
Features
- binomial-normal models for binary data via the
BiBMA
function
NoBMA
and NoBMA.reg()
functions as
wrappers around RoBMA
RoBMA.reg()
functions
for simpler specification of publication bias unadjusted Bayesian
model-averaged meta-analysis
- adding odds ratios output transformation`
- extending (instead of a complete refitting) of models via the
update.RoBMA()
function (only non-converged models by
default or all by setting extend_all = TRUE
)
Fixes
- handling of non-converged models
version 3.0.1
Fixes (thanks to Don & Rens)
- compilation issues with Clang
(https://github.com/FBartos/RoBMA/issues/28)
- lapack path specifications
(https://github.com/FBartos/RoBMA/issues/24)
version 3.0
Features
- meta-regression with
RoBMA.reg()
function
- posterior marginal summary and plots for the
RoBMA.reg
models with summary_marginal()
and
plot_marginal()
functions
- new vignette on hierarchical Bayesian model-averaged
meta-analysis
- new vignette on robust Bayesian model-averaged meta-regression
- adding vignette from AMPPS tutorial
- faster implementation of JAGS multivariate normal distribution
(based on the BUGS JAGS module)
- incorporating
weight
argument in the RoBMA
and combine_data
functions in order to pass
custom
likelihood weights
- ability to use inverse square weights in the weighted meta-analysis
by setting a
weighted_type = "inverse_sqrt"
argument
Changes
- reworked interface for the hierarchical models. Prior distributions
are now specified via the
priors_hierarchical
and
priors_hierarchical_null
arguments instead of
priors_rho
and priors_rho_null
. The model
summary now shows Hierarchical
component summary.
version 2.3.2
Fixes
- suppressing start-up message
- cleaning up imports
version 2.3.1
Fixes
- fixing weighted meta-analysis parameterization
version 2.3
Features
- weighted meta-analysis by specifying
study_ids
argument
in RoBMA()
and setting weighted = TRUE
. The
likelihood contribution of estimates from each study is down-weighted
proportionally to the number of estimates in that study. Note that this
experimental feature is supposed to provide a conservative alternative
for estimating RoBMA in cases with multiple estimates from a study where
the multivariate option is not computationally feasible.
version 2.2.3
Fixes
- updating the Makevars to install with R 4.2 and JAGS 4.3.1
version 2.2.2
Fixes
- updating the C++ to compile on M1 Mac
version 2.2.1
Changes
- message about the effect size scale of parameter estimates is always
shown
- compatibility with BayesTools 0.2.0+
version 2.2
Features
- three-level meta-analysis by specifying
study_ids
argument in RoBMA
. However, note that this is (1) an
experimental feature and (2) the computational expense of fitting
selection models with clustering is extreme. As of now, it is almost
impossible to have more than 2-3 estimates clustered within a single
study).
version 2.1.2
Fixes
- adding Windows ucrt patch (thanks to Tomas Kalibera)
- adding BayesTools version check
version 2.1.1
Fixes
- incorrectly formatted citations in vignettes and capitalization
Features
- adding
informed_prior()
function (from the BayesTools
package) that allows specification of various informed prior
distributions from the field of medicine and psychology
- adding a vignette reproducing the example of dentine sensitivity
with the informed Bayesian model-averaged meta-analysis from Bartoš et
al., 2021 (open-access),
- further reductions of fitted object size when setting
save = "min"
version 2.1
Fixes
- more informative error message when the JAGS module fails to
load
- correcting wrong PEESE transformation for the individual models
summaries (issue #12)
- fixing error message for missing conditional PET-PEESE
- fixing incorrect lower bound check for log(OR)
Features
- adding
interpret()
function (issue #11)
- adding effect size transformation via
output_scale
argument to plot()
and plot_models()
functions
- better handling of effect size transformations and scaling -
BayesTools style back-end functions with Jacobian transformations
version 2.0
Please notice that this is a major release that breaks backwards
compatibility.
Changes
- naming of the arguments specifying prior distributions for the
different parameters/components of the models changed
(
priors_mu
-> priors_effect
,
priors_tau
-> priors_heterogeneity
, and
priors_omega
-> priors_bias
),
- prior distributions for specifying weight functions now use a
dedicated function
(
prior(distribution = "two.sided", parameters = ...)
->
prior_weightfunction(distribution = "two.sided", parameters = ...)
),
- new dedicated function for specifying no publication bias adjustment
component / no heterogeneity component (
prior_none()
),
- new dedicated functions for specifying models with the PET and PEESE
publication bias adjustments
(
prior_PET(distribution = "Cauchy", parameters = ...)
and
prior_PEESE(distribution = "Cauchy", parameters = ...)
),
- new default prior distribution specification for the publication
bias adjustment part of the models (corresponding to the RoBMA-PSMA
model from Bartoš et al., 2021 preprint),
- new
model_type
argument allowing to specify different
“pre-canned” models ("PSMA"
= RoBMA-PSMA, "PP"
= RoBMA-PP, "2w"
= corresponding to Maier et al., in press
, manuscript),
combine_data
function allows combination of different
effect sizes / variability measures into a common effect size measure
(also used from within the RoBMA
function),
- better and improved automatic fitting procedure now enabled by
default (can be turned of with
autofit = FALSE
)
- prior distributions can be specified on the different scale than the
supplied effect sizes (the package fits the model on Fisher’s z scale
and back transforms the results back to the scale that was used for
prior distributions specification, Cohen’s d by default, but both of
them can be overwritten with the
prior_scale
and
transformation
arguments),
- new prior distributions, e.g., beta or fixed weight functions,
- estimates from individual models are now plotted with the
plot_models()
function and the forest plot can be obtained
with the forest()
function,
- the posterior distribution plots for the individual weights are no
able supported, however, the weightfunction and the PET-PEESE
publication bias adjustments can be visualized with the
plot.RoBMA()
function and
parameter = "weightfunction"
and
parameter = "PET-PEESE"
.
version 1.2.1
Fixes
- check_setup function not working at all
version 1.2.0
Changes
- the studies’s true effects are now marginalized out of the random
effects models and are no longer estimated (see Appendix A of our prerint for more details). As a
results, arguments referring to the true effects are now disabled.
- all models are now being estimated using the likelihood of effect
sizes (instead of test-statistics as usually defined). We reproduced the
simulation study that we used to evaluate the method performance and it
achieved identical results (up to MCMC error, before marginalizing out
the true effects). A big advantage of using the normal likelihood for
effect sizes is a considerable speed up of the whole estimation
process.
- as a results of these two changes, the results of the models will
differ to those of pre 1.2.0 version
Fixes
- autofit being turn on if any control argument was specified
version 1.1.2
Fixes
- vdiffr not being used conditionally in unit tests
version 1.1.1
Fixes
- inability to fit a model without specifying a seed
- inability to produce individual model plots due to incompatibility
with the newer versions of ggplot2
version 1.1.0
Features
- parallel within and between model fitting using the parallel package
with ‘parallel = TRUE’ argument
version 1.0.5
Fixes:
- models being fitted automatically until reaching R-hat lower than
1.05 without setting max_rhat and autofit control parameters
- bug preventing to draw a bivariate plot of mu and tau
- range for parameter estimates from individual models no containing 0
(or 1 in case of OR measured effect sizes)
- inability to fit a model with only null mu distributions if
correlation or OR measured effect sizes were specified
- ordering of the estimated and observed effects when both of them are
requested simultaneously
- formatting of this file (NEWS.md)
Improvements:
- priors plot: parameter specification, default plotting range,
clearer x-axis labels in cases when the parameter is defined on
transformed scale
- parameters plots: probability scale always ends at the same spot as
is the last tick on the density scale
- adding warnings if any of the specified models has Rhat higher than
1.05 or the specified value
- grouping the same warnings messages together
version 1.0.4
Fixes:
- inability to run models without the silent = TRUE control
version 1.0.3
Features:
- x-axis rescaling for the weight function plot (by setting ‘rescale_x
= TRUE’ in the ‘plot.RoBMA’ function)
- setting expected direction of the effect in for RoBMA function
Fixes:
- marginal likelihood calculation for models with spike prior
distribution on mean parameter which location was not set to 0
- some additional error messages
CRAM requested changes:
- changing information messages from ‘cat’ to ‘message’ from plot
related functions
- saving and returning the ‘par’ settings to the user defined one in
the base plot functions
version 1.0.2
Fixes:
- the summary and plot function now shows quantile based confidence
intervals for individual models instead of the HPD provided before (this
affects only ‘summary’/‘plot’ with ‘type = “individual”’, all other
confidence intervals were quantile based before)
version 1.0.1
Fixes:
- summary function returning median instead of mean
version 1.0.0 (vs the osf
version)
Fixes:
- incorrectly weighted theta estimates
- models with non-zero point prior distribution incorrectly plotted
using when “models” option in case that the mu parameter was
transformed
Additional features:
- analyzing OR
- distributions implemented using boost library (helps with
convergence issues)
- ability to mute the non-suppressible “precision not achieved”
warning messages by using “silent” = TRUE inside of the control
argument
- vignettes
Notable changes:
- the way how the seed is set before model fitting (the simulation
study will not be reproducible with the new version of the package)