Changes in version 0.1.0 (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

  - Composable geometries: geom_forest_ci(), geom_forest_diamond(),
    geom_forest_ref(), geom_forest_predict(), geom_forest_text(), and
    geom_funnel_contour(); helpers tidy_meta(), fortify.meta(), and
    format_effect(); themes theme_forest() and theme_funnel().
  - ggforest() and ggfunnel() take per-element styling arguments (for
    example predict_args, diamond_colours, ci_args, ref_args,
    point_args, contour_args) to restyle any built-in layer.
  - Every summary measure is back-transformed with its correct inverse
    (exponentiation for ratios, inverse-logit for logit proportions,
    Fisher's z for correlations, and so on).