Skip to contents

Changelog

Source: NEWS.md

ggmeta 0.1.1

CRAN release: 2026-09-22

Bug-fix release. The display of single-group measures (proportions and rates) and of generalised linear mixed models now follows meta::forest().

Bug fixes

  • The heterogeneity caption no longer prints p = NA for a generalised linear mixed model. A GLMM tests heterogeneity twice, so meta stores Q, df.Q and pval.Q as a Wald and a likelihood-ratio pair; the whole pair reached the caption, which printed both Q values and lost the p-value. ggforest() now takes the first element of each, the Wald test, as meta::forest() does for its own one-line caption.
  • Study estimates of single-group measures are now the observed values, as in meta::forest(). meta::metaprop() and meta::metarate() store a continuity-corrected TE for studies with zero or all events, so back-transforming it put the point outside its own exact interval: 12 of 12 events was drawn at 0.96 against an upper limit of 1.00. tidy_meta() now uses event / n and event / time for every study of these types, independently of whether a correction was applied. The analysis scale (back_trans = "none") is unaffected.
  • Studies of a generalised linear mixed model (method = "GLMM") no longer get invented weights. meta leaves w.common and w.random empty for such fits because the model does not weight studies by inverse variance, and meta::forest() drops the weight column accordingly; tidy_meta() used to fall back to 1 / seTE^2, so ggforest() printed percentages the model never used. The weight cells are now blank and the squares are equally sized.
  • geom_forest_ci() draws equal, mid-sized squares when no study has a usable weight, instead of shrinking every square to the minimum size. Mixed weights are unaffected: a missing weight alongside usable ones still gets the minimum.
  • Zero and unusable study weights are now formatted sensibly in the table columns. A zero weight prints as 0.0%; a negative or infinite weight is reported once with a message and left blank instead of printing e.g. -25.0% and rescaling another study to 125.0%. Percentages are computed from the positive weights only.
  • tidy_meta() and ggforest() no longer fail on meta objects that contain studies excluded from pooling, such as double-zero studies in meta::metabin(). The number of study rows was taken from x$k, which counts only contributing studies, so building the tidy data frame stopped with “arguments imply differing number of rows”. Such studies are now shown with their label and no interval, as in meta::forest().
  • On-the-fly pooling of a data frame (add_summary = TRUE) now works on the log scale for ratio measures. ggforest(df, add_summary = TRUE, null_effect = 1) log-transforms the estimates and confidence limits before inverse-variance / DerSimonian-Laird pooling and exponentiates the summary back; tidy_meta() gains log_scale for the same purpose. Previously ratio values were pooled on the natural scale and their standard errors were recovered from asymmetric intervals, which gave wrong summaries.
  • The heterogeneity caption of ggforest() prints p < 0.001 instead of p = 0.000 for very small p-values.
  • Table columns (columns = TRUE) no longer print a negative zero (-0.00) for values that round to zero.
  • geom_forest_ci() square sizes now follow the documented mapping min + (max - min) * sqrt(weight / max(weight)). The computed sizes were previously mapped with after_stat() and therefore rescaled a second time by ggplot2’s default size scale, which compressed the differences between studies. A constant or mapped size still overrides the weight-based size.

Minor improvements

  • ggforest()/tidy_meta() pooling and ggfunnel() now report how many studies were left out (missing or non-finite estimate, or a standard error that is not positive) instead of dropping them silently.
  • New tests document the behaviour at boundary conditions (missing or non-positive weights, missing confidence limits or estimates, single-study pooling, funnel plots without usable studies, single-group measures).
  • The README gained a package hex-sticker logo and a CRAN status badge.

ggmeta 0.1.0

CRAN release: 2026-07-22

First CRAN release.

ggmeta extends ‘ggplot2’ to build publication-quality forest and funnel plots from meta objects or tidy data frames. Every plot is an ordinary ggplot, so it can be themed, composed (for example a forest and a funnel plot side by side with patchwork), and saved like any other.

Forest plots

  • ggforest() draws a forest plot from a meta object or a tidy data frame, with study confidence intervals and weight-proportional squares, common- and random-effects summary diamonds, prediction intervals, and null-effect and consensus reference lines.
  • columns = TRUE adds a meta::forest()-style table of effect-estimate, 95% CI, and weight columns (or a chosen subset), aligned on both linear and log axes.
  • add_summary = TRUE pools a tidy data frame of effect sizes on the fly (inverse-variance common effect and DerSimonian-Laird random effects), so a summary diamond can be drawn without the meta package.
  • Journal presets layout_jama(), layout_bmj(), and layout_revman5().

Funnel plots

  • ggfunnel() draws a funnel plot (study effect against standard error) with pseudo confidence-interval contours, from a meta object or a tidy data frame. Ratio, proportion, rate, and correlation measures are drawn on their analysis scale but labelled with back-transformed values.

Building blocks and customisation