Abc: An R package for approximate Bayesian computation (ABC)

Authors: Csillery, Katalin and Francois, Olivier and Blum, Michael G.B. Year: 2012 Journal: Methods in Ecology and Evolution

Abstract

1. Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian computation (ABC) is devoted to these complex models because it bypasses the evaluation of the likelihood function by comparing observed and simulated data. 2.We introduce the R package ‘abc’ that implements several ABC algorithms for performing parameter estimation and model selection. In particular, the recently developed nonlinear heteroscedastic regression methods for ABC are implemented. The ‘abc’ package also includes a cross-validation tool for measuring the accuracy of ABC estimates and to calculate the misclassification probabilities when performing model selection. The main functions are accompanied by appropriate summary and plotting tools. 3.R is already widely used in bioinformatics and several fields of biology. The R package ‘abc’ will make the ABC algorithms available to a large number of R users. ‘abc’ is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/abc/index.html. extcopyright 2012 The Authors. Methods in Ecology and Evolution extcopyright 2012 British Ecological Society.

Notes

Extracted Concepts

• ABC bypasses likelihood evaluation through data simulation
• ABC enables model selection through comparison of observed and simulated data
• Generalist ABC package accommodates flexible model and algorithm choice
• Nonlinear heteroscedastic regression improves ABC parameter estimation accuracy
• User-defined data simulation enables generalist ABC implementation