# Approximate Bayesian Computation via Random Forests

## Documentation for package ‘abcrf’ version 1.4

- DESCRIPTION file.

## Help Pages

abcrf | Create an ABC-RF object: a classification random forest from a reference table towards performing an ABC model choice
abcrf.formula | Create an ABC-RF object: a classification random forest from a reference table towards performing an ABC model choice
covRegAbcrf | Predict posterior covariance between two parameters for new data using two reg-ABC-RF objects
covRegAbcrf.regAbcrf | Predict posterior covariance between two parameters for new data using two reg-ABC-RF objects
densityPlot | Plot the posterior density given a new summary statistic
densityPlot.regAbcrf | Plot the posterior density given a new summary statistic
err.abcrf | Calculate and plot for different numbers of tree, the out-of-bag errors associated with an ABC-RF object
err.regAbcrf | Calculate and plot for different numbers of tree, the out-of-bag mean squared errors associated with a REG-ABC-RF object
plot.abcrf | Plot of an ABC-RF object
plot.regAbcrf | Plot of a reg-ABC-RF object
predict.abcrf | Predict and evaluate the posterior probability of the MAP for new data using an ABC-RF object
predict.regAbcrf | Predict posterior expectation, median, variance and quantiles given a new dataset using a reg-ABC-RF object
readRefTable | Read a reference table simulated from DIYABC
regAbcrf | Create a reg-ABC-RF object: a regression random forest from a reference table aimed out predicting posterior expectation, variance and quantiles for a parameter
regAbcrf.formula | Create a reg-ABC-RF object: a regression random forest from a reference table aimed out predicting posterior expectation, variance and quantiles for a parameter
snp | A simulated example in population genetics
snp.obs | A simulated example in population genetics
variableImpPlot | Variable importance plot from a random forest