1.Approximate Bayesian computation (ABC), also called likelihood-free inference, is a family of statistical techniques to perform parameter inference and model comparison. It is increasingly used in ecology and evolution, where the models used can be too complex to be handled with standard likelihood techniques. The essence of ABC techniques is to compare model simulation outputs to observed data, in order to select the model simulations (and their associated parameter values) which best fit the data. ABC techniques therefore require a large number of model simulations. 2.We introduce the R package 'EasyABC' that enables to launch a series of simulations of a computer code from the R platform and to retrieve the simulation outputs in an appropriate format for post-processing treatments. The 'EasyABC' package further implements several efficient parameter sampling schemes to speed up the ABC procedure: on top of the standard prior sampling, it implements various algorithms to perform sequential (ABC-sequential) and Markov chain Monte Carlo (ABC-MCMC) sampling schemes. The package functions can furthermore make use in parallel of several cores of a multi-core computer. 3.The R package 'EasyABC' complements the package 'abc' which enables various post-processing treatments of simulation outputs. 'EasyABC' will make the up-to-date ABC implementations available to the large community of R users in the fields of ecology and evolution. It is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/EasyABC/index.html .