Software
A number of software packages are currently available for application of ABC to particular classes of statistical models. An assortment of ABC-based software is presented in Table 3.
Software | Keywords and features | Reference |
---|---|---|
DIY-ABC | Software for fit of genetic data to complex situations. Comparison of competing models. Parameter estimation. Computation of bias and precision measures for a given model and known parameters values. | |
ABC R package | Several ABC algorithms for performing parameter estimation and model selection. Nonlinear heteroscedastic regression methods for ABC. Cross-validation tool. | |
ABC-SysBio | Python package. Parameter inference and model selection for dynamical systems. Combines ABC rejection sampler, ABC SMC for parameter inference, and ABC SMC for model selection. Compatible with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models. | |
ABCtoolbox | Open source programs for various ABC algorithms including rejection sampling, MCMC without likelihood, a particle-based sampler, and ABC-GLM. Compatibility with most simulation and summary statistics computation programs. | |
msBayes | Open source software package consisting of several C and R programs that are run with a Perl "front-end". Hierarchical coalescent models. Population genetic data from multiple co-distributed species. | |
PopABC | Software package for inference of the pattern of demographic divergence. Coalescent simulation. Bayesian model choice. | |
ONeSAMP | Web-based program to estimate the effective population size from a sample of microsatellite genotypes. Estimates of effective population size, together with 95% credible limits. | |
ABC4F | Software for estimation of F-statistics for dominant data. | |
2BAD | 2-event Bayesian ADmixture. Software allowing up to two independent admixture events with up to three parental populations. Estimation of several parameters (admixture, effective sizes, etc.). Comparison of pairs of admixture models. |
The suitability of individual software packages depends on the specific application at hand, the computer system environment, and the algorithms required.
Read more about this topic: Approximate Bayesian Computation