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.
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.layout_jama(), layout_bmj(), and layout_revman5().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.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.