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.
Bayesian, Distributions, Econometrics, Environmetrics, Multivariate, NumericalMathematics, Pharmacokinetics, Psychometrics, Robust, SocialSciences
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Nunes & Prangle, "abctools: An R Package for Tuning Approximate Bayesian Computation Analyses", The R Journal, 2015
BibTeX citation
@article{RJ-2015-030,
author = {Nunes, Matthew A. and Prangle, Dennis},
title = {abctools: An R Package for Tuning Approximate Bayesian Computation Analyses},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-030},
doi = {10.32614/RJ-2015-030},
volume = {7},
issue = {2},
issn = {2073-4859},
pages = {189-205}
}