cosmoabc: A Python ABC sampler package which enables parameter inference using an Approximate Bayesian Computation (ABC) algorithm, as described in Ishida et al. 2015.

abc-sde: A MATLAB toolbox for approximate Bayesian computation (ABC) in stochastic differential equation models
implementing the methods of Inference for SDE models via Approximate Bayesian Computation article of Umberto Picchini (2013).

EP-ABC: A Matlab package implementing the methods of EP-ABC article of Barthelmé  and Chopin (2012).

R Package abc:  (by Csilléry, Blum, and François).  The ‘abc’ package provides various functions for parameter estimation and model selection in an ABC framework. Three main functions are available: (i) ‘abc’ implements several ABC inference algorithms, (ii) ‘cv4abc’ is a cross-validation tool to evaluate the quality of the estimation and help the choice of tolerance rate, and (iii) ‘postpr’ implements model selection in an ABC setting. All these functions are accompanied by appropriate summary and plotting functions.

DIY-ABC: (by Cornuet, Santos, and Estoup): A user-friendly approach to Approximate Bayesian Computation for inference on population history using molecular markers. Complex genealogical models, Graphical User Interface, model choice. (Is having a major update in the form of migrating the program to a different language).

ABCtoolbox: a versatile toolkit for approximate Bayesian computations. (Wegmann, D., C. Leuenberger, S. Neuenschwander and L. Ex­coffier)

ABC_distrib: local linear regression of Beaumont et al. (2002) in rejection algorithm context, written in R.

ABCreg: Local linear regression of Thornton (2009).

ABC-SysBio: Sequential Monte Carlo approach of Toni et al. (2009), can do model choice.

2BAD: Estimation of genetic admixture parameters of Bray et al. (2010).

Bayesian Serial SimCoal: An extension of Serial SimCoal which simulates genealogical data, with an ABC analysis feature.

msABC: Facilitates performing ABC with genetic data simulated under ms of Hudson (2002).

msBayes: Statistical phylogeography with data simulated under ms, based on Hickerson et al. (2006).

PopABC: Based on Lopes et al. (2009), simulation of genetic data under coalescent and analysis with ABC rejection with regression and a model choice feature.