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abctools: an R package for tuning Approximate Bayesian Computation analyses

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
<mark>Journal publication date</mark>12/2015
<mark>Journal</mark>The R Journal
Issue number2
Volume7
Number of pages17
Pages (from-to)189-205
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Approximate Bayesian Computation (ABC) is a popular family of algorithms which perform
approximate parameter inference when numerical evaluation of the likelihood function is not possible but data can be simulated from the model. They return a sample of parameter values which produce simulations close to the observed dataset. A standard approach is to reduce the simulated and observed datasets to vectors of summary statistics and accept when the difference between these is below a specified threshold. ABC can also be adapted to perform model choice.
In this article, we present a new software package for R, abctools which provides methods for
tuning ABC algorithms. This includes recent dimension reduction algorithms to tune the choice
of summary statistics, and coverage methods to tune the choice of threshold. We provide several
illustrations of these routines on applications taken from the ABC literature.

Bibliographic note

The R Journal is a peer-reviewed publication of the R Foundation for Statistical Computing. Communications regarding this publication should be addressed to the editors. All articles are licensed under the Creative Commons Attribution 3.0 Unported license (CC BY 3.0, http://creativecommons.org/licenses/by/3.0/).