## Introduction

Approximate Bayesian computation (ABC) is increasingly used in ecology and evolution (Beaumont 2010). In these fields, many models are too complex to be handled with standard likelihood or Bayesian techniques, since the computation of the model likelihood can be intractable or prohibitively costly in computing time. In such cases, ABC techniques, a type of likelihood-free inference, can be used to estimate the model parameters. They consist in (i) simulating a very large number (approximately millions) of times the model, with parameter values drawn from a prior distribution, (ii) comparing the simulation outputs to the observed data, using some summary statistics computed from both data and simulations, and (iii) retaining those simulation results and their corresponding parameters for which the predictions differ from the data by less than a threshold value. The best fit simulations are those that differ from the data by less than a threshold value. And the retained parameter values form an approximation of the posterior distribution of the model parameters. Subsequent steps of model checking and model selection are also possible (and recommended) in ABC applications (Csilléry *et al*. 2010).

In the last decade, a number of improvements to this basic ABC scheme have been proposed (reviewed in Marin *et al*. 2012). Some of them focus on post-processing of the posterior distribution, either through local linear regressions (Beaumont, Zhang & Balding 2002) or nonlinear methods (Blum & François 2010). Such post-processing as well as model checking and selection tools have been implemented in the R package ‘abc’ (Csilléry, François & Blum 2012). Other types of improvement have been proposed. Among them, the use of sequential parameter sampling scheme (ABC-sequential) and the coupling to Markov chain Monte Carlo (ABC-MCMC) have received much attention. Sequential Monte Carlo and Markov Chain Monte Carlo algorithms are well known procedures in computational statistics, which have been used outside of the ABC framework for a long time and which have relatively recently been introduced in the ABC context (Marjoram *et al*. 2003; Sisson, Fan & Tanaka 2007). These algorithms are more efficient, since they preferentially sample the interesting areas of the parameter space (of high likelihood).

Some of these technical schemes are currently available in two toolboxes (ABCtoolbox, Wegmann *et al*. 2010 and ABC-SysBio, Liepe *et al*. 2010). ABC-SysBio is designed for a specific class of models encountered in the field of systems biology, while ABCtoolbox is a generic platform written in C++. These toolboxes are very useful for the scientific community, but they do not benefit from the various advantages of a R package, as being easy to use and install, cooperative, and easy to pipeline with other R tools, as the one developed in the package ‘abc’ (Csilléry, François & Blum 2012). Furthermore, new algorithms have been proposed since the publication of the above toolboxes, and would gain at being widely available to the scientific community.

We present a new R package called ‘EasyABC’ that enables one to launch a series of simulations from the R platform and to retrieve the simulation outputs in an appropriate format for post-processing. The simulation code has to be a R function or a binary executable file respecting some minimal compatibility constraints. 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. EasyABC has been tested on Linux and Windows 32. It works with versions of R ≥ 2.15.0.