tidyabc

Approximate Bayesian Computation Made Tidy

tidyabc provides a flexible, intuitive, and modern framework for Approximate Bayesian Computation (ABC). Designed for data-driven science, it allows you to perform Bayesian inference on complex models where the likelihood function is intractable or unknowable — common in fields like epidemiology, ecology, systems biology, and social science.

Unlike traditional ABC tools, tidyabc integrates with the tidyverse ecosystem. Define your simulation model and summary statistics using familiar R functions, leverage powerful dist_fns objects to represent priors and posteriors, and use dplyr-style workflows to manage your ABC iterations. Whether you need a simple rejection sampler or the efficiency of Sequential Monte Carlo (SMC) or Adaptive ABC, tidyabc gives you the tools to build, diagnose, and interpret your inference within a clear, reproducible, and publication-ready workflow.

Installation

tidyabc is hosted on the AI4CI r-universe. Installation from there is as follows:

options(repos = c(
  "ai4ci" = 'https://ai4ci.r-universe.dev/',
  CRAN = 'https://cloud.r-project.org'))

# Download and install tidyabc in R
install.packages("tidyabc")

You can install the development version of tidyabc from GitHub with:

# install.packages("devtools")
devtools::install_github("ai4ci/tidyabc")

Funding

The authors gratefully acknowledge the support of the UK Research and Innovation AI programme of the Engineering and Physical Sciences Research Council EPSRC grant EP/Y028392/1.