  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 evaluations of the likelihood function using
comparisons between observed and simulated summary statistics. We introduce the
R abc package that implements several ABC algorithms for performing parameter
estimation and model selection. In particular, the recently developed
non-linear 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. Considering an example of demographic inference with population
genetics data, we show the potential of the R package.
  R is already widely used in bioinformatics and several fields of biology. The
R abc package will make the ABC algorithms available to the 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.
