abc: an R package for approximate Bayesian computation (ABC)

Authors

  • Katalin Csilléry,

    Corresponding author
    1. Irstea, UR EMGR, 2 rue de la Papeterie, F-38402 Saint Martin d'Hères, France
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  • Olivier François,

    1. Computational and Mathematical Biology Team, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité, Université Joseph Fourier, Grenoble 1, Centre National de la Recherche Scientifique UMR5525, F-38706 La Tronche, France
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  • Michael G. B. Blum

    1. Computational and Mathematical Biology Team, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité, Université Joseph Fourier, Grenoble 1, Centre National de la Recherche Scientifique UMR5525, F-38706 La Tronche, France
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Summary

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.

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