Package: forrest 0.3.0.9000

forrest: Publication-Ready Forest Plots

Creates publication-ready forest plots from any tabular data containing point estimates and confidence intervals. Suitable for visualising results from regression models, meta-analyses, subgroup analyses, or any comparative study. Supports group and subgroup headings, summary estimates displayed as diamonds, grouped estimates with automatic colour and shape mapping, vertical dodging of multiple estimates within the same row, customisable text columns alongside the plot, and optional row striping. Provides a helper to export plots to PDF, PNG, SVG, or TIFF. Built on 'tinyplot' for clean, consistent visual styling with a minimal dependency footprint.

Authors:Lorenzo Fabbri [aut, cre, cph]

forrest_0.3.0.9000.tar.gz
forrest_0.3.0.9000.zip(r-4.7-any)forrest_0.3.0.9000.zip(r-4.6-any)forrest_0.3.0.9000.zip(r-4.5-any)
forrest_0.3.0.9000.tgz(r-4.6-any)forrest_0.3.0.9000.tgz(r-4.5-any)
forrest_0.3.0.9000.tar.gz(r-4.7-any)forrest_0.3.0.9000.tar.gz(r-4.6-any)
forrest_0.3.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
forrest/json (API)

# Install 'forrest' in R:
install.packages('forrest', repos = c('https://lorenzofabbri.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lorenzofabbri/forrest/issues

Pkgdown/docs site:https://lorenzofabbri.github.io

On CRAN:

Conda:

epidemiologyforest-plotsmeta-analysisplottingquarto

5.08 score 2 stars 8 scripts 375 downloads 2 exports 1 dependencies

Last updated from:78da797ec4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK123
source / vignettesOK225
linux-releaseOK112
macos-releaseOK162
macos-oldrelOK219
windows-develOK115
windows-releaseOK105
windows-oldrelOK94
wasm-releaseOK96

Exports:forrestsave_forrest

Dependencies:tinyplot

Forest plots for regression models
Simulated cohort | Multiple predictors from one model | Same predictor across progressive adjustment models | Same predictor across multiple outcomes | Dose-response (exposure categories) | Marginal effects: g-computation | Marginal effects: inverse probability weighting (IPW) | Time-varying exposures and longitudinal studies | Distributed lag models | Life-course analysis: same exposure across developmental periods | Life-course analysis: comparing stages side by side (dodge) | Saving plots
Rendered fromregression.qmdusingquarto::html

Last update: 2026-03-27
Started: 2026-03-25

Forest plots for meta-analyses
Simulated data | Basic meta-analysis forest plot | Adding text columns | Subgroup analyses by region | Structured subgroup analysis with section headers | Multiple exposures: comparing PM~2.5~, NO~2~, and noise | Saving plots
Rendered frommeta.qmdusingquarto::html

Last update: 2026-03-27
Started: 2026-03-25

How forrest works
Design principles | Source files | Starting data | Step 1b — Section expansion via build_sections() | Subsection expansion | Step 3 — Row type classification | Step 8 — Dodge layout | Colour assignment | Section-level text column annotations | Reference-category rows | Summary (diamond) rows | Theme system
Rendered frominternals.qmdusingquarto::html

Last update: 2026-03-27
Started: 2026-03-27

Introduction to forrest
Basic forest plot | Section headers from a grouping column | Two-level hierarchy with subsection | Adding a summary (pooled) estimate | Group colouring | Multiple estimates per row (dodge) | Wide-format text columns alongside dodged CIs | Point shapes | Adding text columns | Section-level annotations in text columns | Alternating row stripes | Themes | Saving plots
Rendered fromintro.qmdusingquarto::html

Last update: 2026-03-27
Started: 2026-03-25