Visualizing Meta-Analyses with Orchard Plots and Prediction Intervals

Generates prediction intervals and orchard plots for meta-analytic and meta-regression models fitted with the 'metafor' package. Orchard plots augment classic forest plots by displaying individual effect sizes together with group means and their confidence and prediction intervals, providing an enhanced visualization of meta-analytic data for ecology, evolution, and beyond. Methods are described in Nakagawa et al. (2023) .

Introducing the Orchard Plot for Meta-analysis

Citing orchaRd

To cite orchaRd 2.0 in publications one can use the following reference:

Shinichi Nakagawa, Malgorzata Lagisz, Rose E. O'Dea, Patrice Pottier, Joanna Rutkowska, Alistair M. Senior, Yefeng Yang, Daniel W.A. Noble. 2023. orchaRd 2.0: An R package for visualizing meta-analyses with orchard plots. Methods in Ecology and Evolution, https://doi.org/10.1111/2041-210X.14152 (preprint = EcoEvoRxiv, https://doi.org/10.32942/X2QC7).

For earlier versions please cite:

Nakagawa, S., Lagisz, M., O'Dea, R. E., Rutkowska, J., Yang, Y., Noble, D. W., & Senior, A. M. (2019). The Orchard Plot: Cultivating Forest Plots for Use in Ecology, Evolution and Beyond. Research Synthesis Methods https://doi.org/10.1002/jrsm.1424 12: 4-12 (preprint = EcoEvoRxiv https://doi.org/10.32942/osf.io/epqa7)

Installation

To install orchaRd use the following code in R:

install.packages("pacman")
pacman::p_load(devtools, tidyverse, metafor, patchwork, R.rsp, emmeans)

devtools::install_github("daniel1noble/orchaRd", force = TRUE)
library(orchaRd)

How to use?

We detail how to use the orchaRd package in the vignette.

Issues with orchaRd 2.0?

Please note that orchaRd 2.0 is still under active development and testing. If you use it, you should check that the results are what you expect. We do have a number of tests already in place, but there may still be situations where it fails. If you find a bug or a situation that doesn't match your expectations let us know by lodging an issue on GitHub.

Changelog

Version 2.2.0 (2026-04-18)

• Breaking change: Renamed tree.order argument to mod.order throughout the package, vignette, and documentation for clarity and consistency.
• Added k.size, est, and est.size arguments to orchard_plot() for flexible annotation of sample size and effect estimates.
• orchard_plot() now preserves the order of moderator levels as they appear in the data (fixes legend/plot alignment, #92).
• Improved whitespace trimming for moderator/group/condition columns to prevent matching errors.
• Enhanced warning for random-slope prediction intervals.
• All @examples lines in documentation now ≤100 characters for CRAN compliance.
• Fixed: angle/rotation for moderator labels works regardless of flip (#33).
• Fixed: k labels are centered when flip=FALSE (#34).
• Fixed: Windows CI convergence issue for HCS models.
• Added new vignette examples for k.size, est, and est.size.
• Added a worked example to the vignette showing how to combine orchard plots with summary tables using broom, gt, and patchwork (closes #93).
• All tests updated and passing (335 tests).
• Version bumped to 2.2.0.
• Fix Windows CI: increase iter limits for gamma2 convergence test
• Fix angle and k-label alignment when flip=FALSE (#33, #34)
• Fix Rd line widths >100 chars and improve random-slope PI warning (#71)
• Fix whitespace in moderator levels & add vignette examples
• Fix orchard_plot() with non-capitalised moderator categories (closes #64)
• Add point.size parameter to orchard_plot() and bubble_plot() (closes #57)
• Fix prediction interval calculation for HCS/GEN models (closes #46)
• Fix for #92

See the vignette for usage examples of new features.