Binomial-Normal Models (#30)

.github/workflows/R-CMD-check.yaml

@@ -83,6 +83,7 @@ jobs:
         run: |
           remotes::install_deps(dependencies = TRUE)
           remotes::install_cran("rcmdcheck")
+          remotes::install_github("FBartos/BayesTools@independent-factor-prior")
         shell: Rscript {0}
 
       - name: Check

.github/workflows/test-coverage.yaml

@@ -77,6 +77,7 @@ jobs:
           install.packages(c("remotes"))
           remotes::install_deps(dependencies = TRUE)
           remotes::install_cran("covr")
+          remotes::install_github("FBartos/BayesTools@independent-factor-prior")
         shell: Rscript {0}
 
       - name: Test coverage

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: RoBMA
 Title: Robust Bayesian Meta-Analyses
-Version: 3.0.1
+Version: 3.1.0
 Maintainer: František Bartoš <[email protected]>
 Authors@R: c( 
     person("František", "Bartoš",     role = c("aut", "cre"),
@@ -40,7 +40,7 @@ NeedsCompilation: yes
 Depends:
     R (>= 4.0.0)
 Imports:
-    BayesTools (>= 0.2.14),
+    BayesTools (>= 0.2.16),
     runjags,
     rjags,
     stats,
@@ -55,6 +55,7 @@ Suggests:
     metaBMA,
     metafor,
     weightr,
+    lme4,
     fixest,
     metadat,
     testthat,

NAMESPACE

@@ -5,7 +5,11 @@ S3method(print,RoBMA)
 S3method(print,marginal_summary.RoBMA)
 S3method(print,summary.RoBMA)
 S3method(summary,RoBMA)
+S3method(update,BiBMA)
 S3method(update,RoBMA)
+export(BiBMA)
+export(NoBMA)
+export(NoBMA.reg)
 export(OR2d)
 export(OR2logOR)
 export(OR2r)
@@ -16,8 +20,10 @@ export(RoBMA.options)
 export(RoBMA.reg)
 export(check_RoBMA)
 export(check_setup)
+export(check_setup.BiBMA)
 export(check_setup.reg)
 export(combine_data)
+export(contr.independent)
 export(contr.meandif)
 export(contr.orthonormal)
 export(d2OR)
@@ -31,6 +37,9 @@ export(diagnostics_trace)
 export(dwnorm)
 export(forest)
 export(interpret)
+export(is.BiBMA)
+export(is.NoBMA)
+export(is.NoBMA.reg)
 export(is.RoBMA)
 export(is.RoBMA.reg)
 export(logOR2OR)
@@ -82,6 +91,7 @@ importFrom(BayesTools,is.prior)
 importFrom(BayesTools,is.prior.PEESE)
 importFrom(BayesTools,is.prior.PET)
 importFrom(BayesTools,is.prior.factor)
+importFrom(BayesTools,is.prior.independent)
 importFrom(BayesTools,is.prior.none)
 importFrom(BayesTools,is.prior.point)
 importFrom(BayesTools,is.prior.simple)

NEWS.md

@@ -1,3 +1,13 @@
+## 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)
@@ -61,7 +71,6 @@
 - adding a vignette reproducing the example of dentine sensitivity with the informed Bayesian model-averaged meta-analysis from Bartoš et al., 2021 ([open-access](https://onlinelibrary.wiley.com/doi/10.1002/sim.9170)),
 - 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
@@ -74,7 +83,6 @@
 - 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.
 
@@ -91,8 +99,6 @@ Please notice that this is a major release that breaks backwards compatibility.
  - 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
@@ -107,7 +113,6 @@ Please notice that this is a major release that breaks backwards compatibility.
 ### Fixes
 - autofit being turn on if any control argument was specified
 
-
 ## version 1.1.2
 ### Fixes
 - vdiffr not being used conditionally in unit tests
@@ -121,7 +126,6 @@ Please notice that this is a major release that breaks backwards compatibility.
 ### 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