The R Journal: article published in 2015, volume 7:2

abctools: An R Package for Tuning Approximate Bayesian Computation Analyses PDF download
Matthew A. Nunes and Dennis Prangle , The R Journal (2015) 7:2, pages 189-205.

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.

Received: 2015-03-25; online 2015-07-29
CRAN packages: abctools, abc, easyABC, MASS
CRAN Task Views implied by cited CRAN packages: Bayesian, Distributions, Econometrics, Environmetrics, Multivariate, NumericalMathematics, Pharmacokinetics, Psychometrics, Robust, SocialSciences

CC BY 4.0
This article is licensed under a Creative Commons Attribution 3.0 Unported license .

  author = {Matthew A. Nunes and Dennis Prangle},
  title = {{abctools: An R Package for Tuning Approximate Bayesian
          Computation Analyses}},
  year = {2015},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2015-030},
  url = {},
  pages = {189--205},
  volume = {7},
  number = {2}