Update References

README.md

@@ -0,0 +1,66 @@
+# The Meta-Analysis Module <img src='inst/icons/meta-analysis.svg' width='149' height='173' align='right'/>
+
+**JASP Meta-Analysis module** is an add-on module for JASP that provides comprehensive tools for synthesizing evidence across multiple studies. The Meta-Analysis module offers a wide range of functionalities, including (but not limited to) classical and Bayesian meta-analytic approaches, multilevel and multivariate models, meta-regression, publication bias adjustment, and robust meta-analytic methods. Specifically, it comprises analysis tools for effect size computation, random-effects and fixed-effects meta-analysis, moderator analysis, heterogeneity assessment, and publication bias detection. Furthermore, the module provides state-of-the-art Bayesian meta-analytic methods through model averaging and hypothesis testing. The module integrates comprehensive visualizations including forest plots, funnel plots, and bubble plots, along with extensive diagnostic tools that assist researchers in understanding, documenting, and communicating their meta-analytic results.
+
+## Articles
+
+The main functions of the Meta-Analysis module are comprehensively documented in two key manuscripts that serve as the primary references for understanding its functionality and statistical methodology:
+
+**Part I: Classical Approaches** - This article provides detailed coverage of the classical meta-analytic methods implemented in the module, including effect size computation, random-effects models, meta-regression, multilevel and multivariate models.
+[![arXiv](https://img.shields.io/badge/arXiv-2509.09845-b31b1b.svg)](https://doi.org/10.48550/arXiv.2509.09845)
+
+**Part II: Bayesian Approaches** - This article focuses on the advanced Bayesian meta-analytic functionality, covering Bayesian parameter estimation, hypothesis testing through Bayes factors, Bayesian model averaging, and robust Bayesian meta-analysis.
+[![arXiv](https://img.shields.io/badge/arXiv-2509.09850-b31b1b.svg)](https://doi.org/10.48550/arXiv.2509.09850)
+
+
+## R Packages <img src='https://www.r-project.org/logo/Rlogo.svg' width='100' height='78' align='right'/>
+
+The meta-analytic functionality is served by several R packages
+
+- **metafor** - The primary package for classical meta-analytic methods ([metafor on CRAN](https://cran.r-project.org/package=metafor))
+- **RoBMA** -  The primary package for Bayesian meta-analytic methods ([RoBMA on CRAN](https://cran.r-project.org/package=RoBMA))
+- **pema** -  The package for penalized meta-analysis ([pema on CRAN](https://cran.r-project.org/package=pema))
+- **metamisc** -  Package for meta-analysis of prediction model performance ([metamisc on CRAN](https://cran.r-project.org/package=metamisc))
+- **metaSEM** - Package for meta-analytic SEM and SEM-based meta-analysis ([metaSEM on CRAN](https://cran.r-project.org/package=metaSEM))
+
+## Analyses
+
+The organization of the analyses within the Meta-Analysis module in JASP is as follows:
+
+```
+--- Meta-Analysis
+    -- Miscellaneous
+       - Effect Size Computation
+       - Funnel Plot
+    -- Classical
+       - Meta-Analysis
+       - Meta-Analysis (Multilevel/Multivariate)
+       - Mantel-Haenszel / Peto
+       - Prediction Model Performance
+       - WAAP-WLS
+       - PET-PEESE
+       - Selection Models
+       - Meta-Analytic SEM
+       - SEM-Based Meta-Analysis
+    -- Bayesian
+       - Meta-Analysis
+       - Meta-Analysis (Deprecated)
+       - Binomial Meta-Analysis
+       - Penalized Meta-Analysis
+       - Prediction Model Performance
+       - Robust Bayesian Meta-Analysis
+```
+
+### Key Features
+
+**Effect Size Computation**: Calculate standardized effect sizes from raw data including standardized mean differences, odds ratios, correlations, and risk ratios with automatic standard error computation.
+
+**Classical Meta-Analysis**: Comprehensive implementation of random-effects and fixed-effects models with support for meta-regression, subgroup analysis, heterogeneity assessment, and publication bias detection through funnel plot asymmetry tests.
+
+**Advanced Models**: Multilevel models for dependent effect sizes, multivariate models for correlated outcomes, location-scale models for heterogeneity moderation, and cluster-robust standard errors.
+
+**Bayesian Methods**: State-of-the-art Bayesian meta-analysis with model averaging, hypothesis testing via Bayes factors, prior specification options, and robust publication bias adjustment.
+
+**Visualization**: Publication ready forest plots, funnel plots, bubble plots for meta-regression, and comprehensive diagnostic plots including Baujat plots and profile likelihood plots.
+
+**Publication Bias**: Multiple approaches including trim-and-fill, PET-PEESE, selection models, and robust Bayesian meta-analysis.

inst/help/EffectSizeComputation.md

@@ -7,7 +7,7 @@ Already included effect sizes can be passed forward using the Reported effect si
 
 The selected effect size can be computed only for a subset of the dataset using the Subset indicator variable.. 
 
-See [metafor's documentation](https://wviechtb.github.io/metafor/reference/escalc.html) for more detail about the effect sizes.
+See [metafor's documentation](https://wviechtb.github.io/metafor/reference/escalc.html) for more detail about the effect sizes. See [this tutorial](https://doi.org/10.48550/arXiv.2509.09845) for a detailed introduction to the module.
 
 
 #### Design

inst/qml/BayesianBinomialMetaAnalysis.qml

@@ -28,8 +28,10 @@ Form
 	"The analysis provides pre-specified prior distributions for different effect size measures and fields. " + 
 	"The analysis allows you to specify meta-regression, 3-level meta-analysis, and subgroup analysis. " +
 	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.\n\n" +
-	"The analysis is based on the Bayesian meta-analysis/meta-regression parameterization as outlined in Bartoš et al. (2025) with the binomial-normal model described in Bartoš et al. (2023).")
+	"The analysis is based on the Bayesian meta-analysis/meta-regression parameterization as outlined in Bartoš et al. (2025) with the binomial-normal model described in Bartoš et al. (2023).\n\n" + 
+	"See [this tutorial](https://doi.org/10.48550/arXiv.2509.09850) for a detailed introduction to the module.")
 	infoBottom: "## " + qsTr("References") + "\n" +
+	"- Bartoš F & Wagenmakers EJ (2025). “Meta-analysis with JASP, Part II: Bayesian approaches.” _ArXiv Preprint_. https://doi.org/10.48550/arXiv.2509.09850\n" + 
 	"- Bartoš F, Gronau QF, Timmers B, Otte WM, Ly A, Wagenmakers EJ (2021). “Bayesian model‐averaged meta‐analysis in medicine.” _Statistics in Medicine, 40_(30), 6743-6761. https://doi.org/10.1002/sim.9170\n" +
 	"- Bartoš F, Otte WM, Gronau QF, Timmers B, Ly A, Wagenmakers EJ (2023). “Empirical prior distributions for Bayesian meta-analyses of binary and time to event outcomes.” _arXiv Preprint_ https://doi.org/10.48550/arXiv.2306.11468\n" + 
 	"- Bartoš F, Maier M, Stanley TD, Wagenmakers EJ (2025). “Robust Bayesian meta-regression: Model-averaged moderation analysis in the presence of publication bias.” _Psychological Methods_.  https://doi.org/10.1037/met0000737\n" +

inst/qml/BayesianMetaAnalysis.qml

@@ -29,8 +29,10 @@ Form
 	"Additional care is required when using a different type of effect sizes as the default prior distributions might not match the proper scaling of the effect size and heterogeneity. " + 
 	"The analysis allows you to specify meta-regression, 3-level meta-analysis, and subgroup analysis. " +
 	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.\n\n" +
-	"The analysis is based on the Bayesian meta-analysis/meta-regression parameterization as outlined in Bartoš et al. (2025).")
+	"The analysis is based on the Bayesian meta-analysis/meta-regression parameterization as outlined in Bartoš et al. (2025).\n\n" + 
+	"See [this tutorial](https://doi.org/10.48550/arXiv.2509.09850) for a detailed introduction to the module.")
 	infoBottom: "## " + qsTr("References") + "\n" +
+	"- Bartoš F & Wagenmakers EJ (2025). “Meta-analysis with JASP, Part II: Bayesian approaches.” _ArXiv Preprint_. https://doi.org/10.48550/arXiv.2509.09850\n" + 
 	"- Bartoš F, Gronau QF, Timmers B, Otte WM, Ly A, Wagenmakers EJ (2021). “Bayesian model‐averaged meta‐analysis in medicine.” _Statistics in Medicine, 40_(30), 6743-6761. https://doi.org/10.1002/sim.9170\n" +
 	"- Bartoš F, Otte WM, Gronau QF, Timmers B, Ly A, Wagenmakers EJ (2023). “Empirical prior distributions for Bayesian meta-analyses of binary and time to event outcomes.” _arXiv Preprint_ https://doi.org/10.48550/arXiv.2306.11468\n" + 
 	"- Bartoš F, Maier M, Stanley TD, Wagenmakers EJ (2025). “Robust Bayesian meta-regression: Model-averaged moderation analysis in the presence of publication bias.” _Psychological Methods_.  https://doi.org/10.1037/met0000737\n" +

inst/qml/ClassicalMetaAnalysis.qml

@@ -26,12 +26,14 @@ Form
 	info: qsTr("Classical meta-analysis allows you to conduct a meta-analysis using the classical approach. " + 
 	"It provides options for fixed and random effects models, as well as meta-regression, location-scale models, and subgroup analysis. " +
 	"Additional options include the ability to specify clustering for robust variance estimation, permutation tests, and generating the metafor package R code. " +
-	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.")
+	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.\n\n" + 
+	"See [this tutorial](https://doi.org/10.48550/arXiv.2509.09845) for a detailed introduction to the module.")
 	infoBottom: "## " + qsTr("References") + "\n" +
+	"- Bartoš F, Wagenmakers EJ, & Viechtbauer W (2025). “Meta-analysis with JASP, Part I: Classical approaches.” _ArXiv Preprint_. https://doi.org/10.48550/arXiv.2509.09845\n" + 
 	"- Viechtbauer W (2010). “Conducting meta-analyses in R with the metafor package.” _Journal of Statistical Software, 36_(3), 1–48. https://doi.org/10.18637/jss.v036.i03\n" +
 	"- Viechtbauer W, López-López JA, Sánchez-Meca J, Marín-Martínez F (2015). “A comparison of procedures to test for moderators in mixed-effects meta-regression models.” _Psychological Methods, 20_(3), 360–374. https://doi.org/10.1037/met0000023\n" +
 	"- Viechtbauer W, López-López JA (2022). “Location-scale models for meta-analysis.” _Research Synthesis Methods, 13_(6), 697–715. https://doi.org/10.1002/jrsm.1562\n" +
-	"- Viechtbauer W (2025). _metafor: Meta-Analysis Package for R_. R package version 4.8-0 Available at: <https://CRAN.R-project.org/package=metafor>.\n" +
+	"- Viechtbauer W (2025). _metafor: Meta-Analysis Package for R_. R package version 4.8-0 Available at: <https://CRAN.R-project.org/package=metafor>.\n" + 
 	"## " + qsTr("R Packages") + "\n" +
 	"- metafor"
 

inst/qml/ClassicalMetaAnalysisMultilevelMultivariate.qml

@@ -27,8 +27,10 @@ Form
 	"The effect size dependency can be adjusted for by specifying the 'Effect Size Variance-Covariance Matrix' (interfacing the 'vcalc' function) and specifying the 'Random Effects/Model Components' (interfacing the 'random' argument in 'rma.mv' function). " +
 	"It provides options for fixed and random effects models, as well as meta-regression, and subgroup analysis. " +
 	"Additional options include the ability to specify clustering for robust variance estimation, permutation tests, and generating the metafor package R code. " +
-	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.")
+	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.\n\n" + 
+	"See [this tutorial](https://doi.org/10.48550/arXiv.2509.09845) for a detailed introduction to the module.")
 	infoBottom: "## " + qsTr("References") + "\n" +
+	"- Bartoš F, Wagenmakers EJ, & Viechtbauer W (2025). “Meta-analysis with JASP, Part I: Classical approaches.” _ArXiv Preprint_. https://doi.org/10.48550/arXiv.2509.09845\n" + 
 	"- Viechtbauer W (2010). “Conducting meta-analyses in R with the metafor package.” _Journal of Statistical Software, 36_(3), 1–48. https://doi.org/10.18637/jss.v036.i03\n" +
 	"- Viechtbauer W, López-López JA, Sánchez-Meca J, Marín-Martínez F (2015). “A comparison of procedures to test for moderators in mixed-effects meta-regression models.” _Psychological Methods, 20_(3), 360–374. https://doi.org/10.1037/met0000023\n" +
 	"- Viechtbauer W (2025). _metafor: Meta-Analysis Package for R_. R package version 4.8-0 Available at: <https://CRAN.R-project.org/package=metafor>.\n" +

inst/qml/FunnelPlot.qml

@@ -24,8 +24,10 @@ Form
 {
 	info: qsTr("Funnel plots allow you to visualize the observed effect sizes and standard errors with the funnel outlaying the sampling distribution under a given meta-analytic model. " +
 	"Funnel plots are often used to assess publication bias and small-study effects. However, many studies have shown that funnel plots are not very reliable for this purpose. " + 
-	"See e.g. Lau et al., (2006) and Terrin et al. (2005).")
+	"See e.g. Lau et al., (2006) and Terrin et al., (2005).\n\n" + 
+	"See [this tutorial](https://doi.org/10.48550/arXiv.2509.09845) for a detailed introduction to the module.")
 	infoBottom: "## " + qsTr("References") + "\n" +
+	"- Bartoš F, Wagenmakers EJ, & Viechtbauer W (2025). “Meta-analysis with JASP, Part I: Classical approaches.” _ArXiv Preprint_. https://doi.org/10.48550/arXiv.2509.09845\n" + 
 	"- Lau J, Ioannidis JP, Terrin N, Schmid CH, Olkin I. (2006). “The case of the misleading funnel plot.” _BMJ, 333_(7568), 597-600. https://doi.org/10.1136/bmj.333.7568.597\n" +
 	"- Terrin N, Schmid CH, Lau J (2005). In an empirical evaluation of the funnel plot, researchers could not visually identify publication bias. _Journal of Clinical Epidemiology, 58_(9), 894-901. https://doi.org/10.1016/j.jclinepi.2005.01.006\n" +
 	"- Kossmeier M, Tran US, Voracek M. (2020). “Power-enhanced funnel plots for meta-analysis.” _Zeitschrift für Psychologie, 228_(1). https://doi.org/10.1027/2151-2604/a000392\n" +

inst/qml/RobustBayesianMetaAnalysis.qml

@@ -29,8 +29,10 @@ Form
 	"Additional care is required when using a different type of effect sizes as the default prior distributions might not match the proper scaling of the effect size and heterogeneity. " + 
 	"The analysis allows you to specify meta-regression, 3-level meta-analysis, and subgroup analysis. " +
 	"The results include estimates of effect sizes, heterogeneity, moderation, and various plots to visualize the results.\n\n" +
-	"The analysis is based on the Bayesian meta-analysis/meta-regression parameterization as outlined in Bartoš et al. (2025).")
+	"The analysis is based on the Bayesian meta-analysis/meta-regression parameterization as outlined in Bartoš et al. (2025).\n\n" + 
+	"See [this tutorial](https://doi.org/10.48550/arXiv.2509.09850) for a detailed introduction to the module.)"
 	infoBottom: "## " + qsTr("References") + "\n" +
+	"- Bartoš F & Wagenmakers EJ (2025). “Meta-analysis with JASP, Part II: Bayesian approaches.” _ArXiv Preprint_. https://doi.org/10.48550/arXiv.2509.09850\n" + 
 	"- Bartoš F, Gronau QF, Timmers B, Otte WM, Ly A, Wagenmakers EJ (2021). “Bayesian model‐averaged meta‐analysis in medicine.” _Statistics in Medicine, 40_(30), 6743-6761. https://doi.org/10.1002/sim.9170\n" +
 	"- Bartoš F, Maier M, Stanley TD, Wagenmakers EJ (2025). “Robust Bayesian meta-regression: Model-averaged moderation analysis in the presence of publication bias.” _Psychological Methods_.  https://doi.org/10.1037/met0000737\n" +
 	"- Bartoš F, Maier M, Quintana DS, Wagenmakers EJ (2022). “Adjusting for publication bias in JASP and R: Selection models, PET-PEESE, and robust Bayesian meta-analysis.” _Advances in Methods and Practices in Psychological Science, 5_(3), 25152459221109259. https://doi.org/10.1177/25152459221109259\n" +