Reference

Approximate Bayesian Computation
validate_abc()	Validate individual components of an ABC model
simulate_abc()	Simulate data for ABC inference using specified priors
run_abc()	Perform ABC on data generated by simulate_abc
Grid-based inference
simulate_grid()	Simulate values of summary statistics across a parameter grid
Genetic algorithms
compute_fitness()	Compute the fitness of the given parameter set against observed data
run_ga()	Run genetic algorithm (GA) inference of the parameters of the given model
ABC cross-validation and posterior checks
cross_validate()	Perform cross-validation using the anc::cv4postpr function
select_model()	Perform selection between different ABC models
goodness_of_fit()	Peform a test of the goodness-of-fit of a given model
predict(<demografr_abc.abc>)	Generate summary statistics from the inferred posterior distribution of parameters
plot(<demografr_cv>)	Plot results of the cv4postpr procedure from the abc R package
extract_prediction()	Unnest the predicted values of a given statistic from the simulated data
plot_prediction()	Plot the results of a posterior predictive check for a given summary statistic
Analysis of ABC inference results
extract_summary()	Extract table of estimated model parameters
extract_posterior()	Extract inferred posterior(s) as a standard data frame
plot_prior()	Plot prior distribution(s)
plot_posterior()	Plot posterior distribution of given parameters
plot(<demografr_abc.abc>)	Plot diagnostics of posterior distribution(s)
hist(<demografr_abc.abc>)	Plot histogram of posterior distribution(s)
Helper functions
simulate_ts()	Simulate a single tree sequence from the given inference setup
combine_data()	Combine multiple individual ABC simulation runs into one
sample_prior()	Sample value from a given prior sampling formula object