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abc (version 2.0)

Tools for Approximate Bayesian Computation (ABC)

Description

The package implements several ABC algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.

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Version

Install

install.packages('abc')

Monthly Downloads

1,391

Version

2.0

License

GPL (>= 3)

Maintainer

Blum Michael

Last Published

July 11th, 2014

Functions in abc (2.0)

human

A set of R objects containing observed data from three human populations, and simulated data under three different demographic models. The data set is used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details).

gfit

Goodness of fit

ppc

Data to illustrate the posterior predictive checks for the data human. ppc and human are used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details).

summary.postpr

Posterior model probabilities and Bayes factors

summary.cv4abc

Calculates the cross-validation prediction error

gfitpca

Goodness of fit with principal component analysis

plot.cv4abc

Cross-validation plots for ABC

abc

Parameter estimation with Approximate Bayesian Computation (ABC)

plot.cv4postpr

Barplot of model misclassification