The metaUI package allows users to quickly create powerful and customisable Shiny web apps that allow readers to explore a meta-analytic dataset in depth. They can explore the impact of different analytical choices, see results for subgroups of particular interest, and even modify and update the underlying dataset.
The motivation for the package came from the fact that meta-analyses are based on rich datasets that can be analyzed in numerous ways, and it is unlikely that authors and readers will always agree on the “best ways” to analyze the data. Whether it comes to the choice of model (e.g., random versus fixed effects), the methods for assessing or adjusting for publication bias (e.g., z-curve, p-curve, PET/PEESE etc), or the moderators to be considered, disagreements are likely to arise. This can lead to the inclusion of lengthy robustness checks and alternative analyses that are time-consuming and difficult to digest. metaUI, as an R package that supports researchers in creating interactive Shiny web apps can help - these apps allow readers (and reviewers) to explore meta-analytic datasets in a variety of different ways. Apart from allowing readers (and reviewers) to assess the robustness and trustworthiness of results more comprehensively, metaUI apps allow users to assess the results that are most relevant to them, such as by filtering the dataset to focus on a specific group of participants, region, outcome variable, or research method. With the opportunity for users to download the dataset used and to upload alternatives, it will also facilitate the updating of meta-analyses.
The idea came from researchers who have created similar web apps for their meta-analyses that have been well received, yet they require substantial time investment and advanced coding skills to create. With metaUI, researchers can get a working app very swiftly – while they still have the flexibility to tailor the display in line with their interests and requirements. This branch is a tested development candidate with scientific and licensing limits documented below; feedback and contributions are welcome.
You can install the development version of metaUI from GitHub with:
if (!require(remotes)) install.packages("remotes")
remotes::install_github("LukasWallrich/metaUI")The best way to get started in RStudio is with the metaUI template - so in RStudio, go to File -> New File -> R Markdown then select From Template and Create a metaUI Shiny app. To see the template in the most readable format, select Visual just above the file that has opened to enter the Visual Editor. Then you can follow the step-by-step instructions.
Outside R Studio, you can run file.edit(system.file("rmarkdown/templates/create-a-metaui-shiny-app/skeleton/skeleton.Rmd", package = "metaUI")) to open the template file and then take it from there.
The Getting started vignette provides a step-by-step example ... or you may want to watch this video tutorial for a step-by-step walk-through.
Much of the package code was based on two previous interactive meta-analyses apps:
- Röseler, L., Körner, R., & Schütz, A. (2021). Dynamic Meta-Analysis. Retrieved from https://osf.io/ns65r/, Shiny app accessible here
- Röseler, L., Weber, L., Helgerth, K. A. C., Stich, E., Günther, M., Tegethoff, P., Wagner, F. S., Ambrus, E., Antunovic, M., Barrera, F., Halali, E., Ioannidis, K., McKay, R., Milstein, N., Molden, D. C., Papenmeier, F., Rinn, R., Schreiter, M. L., Zimdahl, M., Allen, E., Bahník, S., Baumeister, R. F., Bermeitinger, C., Bickenbach, S. L. C., Blank, P. A., Blower, F. B. N., Bögler, H. L., Boo, F. L., Boruchowicz, C., Bühler, R. L., Burgmer, P., Cheek, N., N., Dohle, S., Dorsch, L., Dück, M. S., Fels, S.-A., Fischer, A. L., Frech, M.-L., Freira, L., Friedinger, K., Genschow, O., Harris, A., Hartig, B., Häusser, J. A., Hedgebeth, M., Henkel, M., Horvath, D., Hügel, J. C., Igna, E. L. E., Imhoff, R., Intelmann, P., Karg, A. H., Klamar, A., Klein, C., Klusmann, B., Knappe, E., Köppel, L.-M., Koßmann, L., Kraft, P., Kroworsch, M. K., Krueger, S. M., Kühling, S., Lagator, S., Lammers, J., Loschelder, D. D., Navajas, J., Norem, J., K., Novak, J. Onuki, Y., Page, E., Panse, F., Pavlovic, Z., Pearton, J., Rebholz, T. R., Rodgers, S., Röseler, J. J., Rostekova, A., Roßmaier, K. V., Sartorio, M., Scheelje, L., Schindler, S., Schreiner, N. B., Seida, C., Shanks, D. R., Siems, M.-C., Stitz, M., Starkulla, M., Stäglich, M., Thies, K., Thum, E., Undorf, M., Unger, B. D., Urlichich, D., Vadillo, M. A., Wackershauser-Sablotny, V., Wessel, I., Wolf, H., Zhou, A., & Schütz, A. (2022). OpAQ: Open Anchoring Quest, Version 1.1.48.95. https://dx.doi.org/10.17605/OSF.IO/YGNVB, Shiny app accessible here
The introductory sample is the CC-BY-4.0 mental-health pool from Dannheim et al.
(2025), doi:10.5271/sjweh.4219.
Its 12 signed Hedges-g effects, CI-derived SEs, membership and changes are
attributed in inst/examples/dannheim. Existing Aksayli/Coles data retain their
verified source licences and attribution in inst/COPYRIGHTS.
- Allbritton et al. created A General Tool for Living Meta-Analysis to run and update meta-analyses online, which contains similar functionalities. Currently, it seems more feature-rich than metaUI, but more focused on meta-analysts rather than end users of insights - and less customisable. The app is accessible here, more details are in the accompanying paper:
Allbritton, D., Gómez, P., Angele, B., Vasilev, M., & Perea, M. (2024). Breathing Life Into Meta-Analytic Methods. Journal of Cognition, 7(1).
Other related tools include Metapsy (analysis workflow inspiration), the Cooperation Databank (dataset exploration), PsychOpen CAMA (cumulative meta-analysis), and the taVNS HRV app (a Bayesian living meta-analysis). These are references, not endorsements or sources of bundled code. See the issue follow-up decisions for the assessed feature overlap and remaining work.
The development branch adds a versioned JSON build path while retaining editable R/Shiny apps. See reproduction and scientific contract, the tiny synthetic config, and its JSON schema. Build and launch are separate:
First copy tiny.json and tiny.csv from inst/examples into a fresh, empty folder, then run:
metaUI::build_app("tiny.json")
shiny::runApp("tiny-app") # separate, blocking launch actiongenerate_shiny() also supports this workflow: supply save_to_folder and
launch_app = FALSE, then launch separately with shiny::runApp("<folder>"), passing the folder you
saved to (a bare shiny::runApp() runs the current working directory). With
launch_app = TRUE (the default when no folder is supplied), interactive printing
of the returned app launches Shiny and blocks the console until you stop the app.
Assigning the app object to a variable postpones launch until you print it.
Authors may name one primary model (primary_model in generate_shiny() or the
JSON config). Apps show it first and label the other rows as explorations; they never
substitute another model when it is unsupported.
Generation refuses a nonempty folder by default. p/N are optional for the primary models; directional models require explicit direction. COR/ZCOR require explicit variance scales. Read the generated validation report before interpreting results. The introductory Dannheim data use signed Hedges g, not correlations. No public deployment is automatic.
PsychOpen CAMA is another platform for cumulative meta-analysis. metaUI's intended contribution is an author-owned app with editable R source. See the bounded publication-readiness assessment.
Copy inst/examples/dannheim to a fresh working directory and run
Rscript --vanilla build.R. Launch separately with
shiny::runApp("mental-health-app"). The tiny CC-BY mental-health pool retains
12 exact benchmark member IDs and original signed Hedges g with CI-derived
native SE. The source-reference REML/Hartung–Knapp model is checked against
metafor; other models/diagnostics are explicitly exploratory. Read the example's
README/provenance before interpreting results. No benchmark corpus is deployed.
Generated apps reuse compatible fitted objects and retain three recent exact
fit selections per reader session; cache_hit discloses reuse. Custom models
default to no cache. Pass options = list(fit_cache_entries = 0) to generate_shiny() (or set
"options": {"fit_cache_entries": 0} in the build JSON) to disable caching;
keep it disabled for random models or code depending on external state. Uploads
are validated, stored in session state, and clear prior cached results.
Performance measurements distinguish faster p-curve calculations/fit reuse from cache hits and first-time fitting costs. Licence audit records the licensed replacement of the former Barroso sample. This development candidate is not a public release.
Downloads preserve the full current input dataset and saved filter selections; the summary sheet describes the selected rows. An empty selection stays empty on re-upload and reports no eligible rows, while the original inputs remain available.
Summary includes an interval assessment for a reader-chosen symmetric smallest
effect size of interest, on the displayed SMD or r scale. No bound is assumed.
The strict containment criterion uses the reported intervals without refitting;
model assumptions and small-sample limitations are shown beside the assessment.
Downloads record the bound and classification in an equivalence sheet.
Forest plots compare observed-effect intervals and available multilevel/RVE summary intervals. PDF, PNG and CSV downloads use the same selected rows; the CSV retains both fitting and displayed scales. The configured row limit also applies to exports.
The synthetic correlation example demonstrates raw-r variance conversion and checks the fitted summary against an independent metafor reference.
Authors can supply up to five alternative computations in prepare_data(alternatives = ...).
Each named entry declares es_field, variance or se, es_type,
variance_scale for correlations, and a justification. primary_label names the
original computation. All alternatives use the same effects and fitting scale.
Readers apply a computation with Analyze data; the comparison tab fits the
configured multilevel/RVE models for the same selection. The descriptive z filter
and source p-values remain anchored to the primary computation.
Enable study-level Bayesian analysis with
generate_shiny(..., bayesian = list(enabled = TRUE)), after installing the
optional bayesmeta package. The mean prior is flat; the heterogeneity prior is
half-normal with scale 0.5 for SMD or 0.25 for Fisher z, configurable with
tau_scale. Central 95% credible intervals and posterior medians are reported,
and the sensitivity tab compares half and double that prior scale. This uses
aggregated studies and the declared within-study correlation. Authors who edit
aggregation_method in the generated labels_and_options.R to "first" switch it
to the first-effect rule; the build records the priors and the default aggregation.
The default cap of 20 studies protects interactive use: first-fit benchmarks took
about 20 seconds at 20 studies and 97 seconds at 50 on the review machine.
Authors can set max_studies from 2 to 50, with a build warning above 20. Each
Analyze blocks the Shiny server process while fitting. Prior sensitivity runs
when its tab or download is requested and can require two extra fits.
See Röver et al. for the prior rationale.
After analysis, the selection link and address bar describe the applied selection on this exact built dataset. Links validate filters, missing-value choices, computation and equivalence bound before restoring and analysing. Invalid links leave a notice and require manual analysis. Uploaded data require the workbook, which includes all computation inputs; their session-specific data are not encoded in URLs. Schema version 2 adds computation and Bayesian authoring; version 1 continues to accept the original configuration fields.