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abc (version 1.6)

Tools for Approximate Bayesian Computation (ABC)

Description

The package implements several ABC algorithms for performing parameter estimation and model selection. 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

1.6

License

GPL (>= 3)

Maintainer

Blum Michael

Last Published

August 14th, 2012

Functions in abc (1.6)

postpr

Estimating posterior model probabilities
abc

Parameter estimation with Approximate Bayesian Computation (ABC)
plot.cv4abc

Cross-validation plots for ABC
cv4abc

Cross validation for Approximate Bayesian Computation (ABC)
summary.abc

Summaries of posterior samples generated by ABC algortithms
plot.abc

Diagnostic plots for ABC
cv4postpr

Leave-one-our cross validation for model selection ABC
summary.cv4abc

Calculates the cross-validation prediction error
hist.abc

Posterior histograms
musigma2

A set of objects used to estimate the population mean and variance in a Gaussian model with ABC.
expected.deviance

Expected deviance
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).
summary.cv4postpr

Confusion matrix and misclassification probabilities of models
plot.cv4postpr

Barplot of model misclassification
summary.postpr

Posterior model probabilities and Bayes factors
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).