Journal
METHODS IN ECOLOGY AND EVOLUTION
Volume 3, Issue 3, Pages 475-479Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.2041-210X.2011.00179.x
Keywords
coalescent; model-based inference; neural networks; population genetics
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Funding
- Universite Joseph Fourier (ABC MSTIC) at the Computational and Mathematical Biology Team (BCM, TIMC-IMAG)
- Ecology and Evolution Laboratory (ENS, Paris) [ANR-06-BDIV-003]
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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 .
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