Function reference • BayesTools

All functions
`BayesTools` `_PACKAGE` `BayesTools-package`	BayesTools
`ensemble_estimates_table()` `ensemble_inference_table()` `ensemble_summary_table()` `ensemble_diagnostics_table()` `ensemble_estimates_empty_table()` `ensemble_inference_empty_table()` `ensemble_summary_empty_table()` `ensemble_diagnostics_empty_table()` `marginal_estimates_table()`	Create BayesTools ensemble summary tables
`model_summary_table()` `runjags_estimates_table()` `runjags_inference_table()` `JAGS_estimates_table()` `JAGS_inference_table()` `JAGS_summary_table()` `model_summary_empty_table()` `runjags_estimates_empty_table()` `runjags_inference_empty_table()` `JAGS_estimates_empty_table()` `JAGS_inference_empty_table()` `stan_estimates_table()`	Create BayesTools model tables
`JAGS_add_priors()`	Add 'JAGS' prior
`JAGS_bridgesampling()`	Compute marginal likelihood of a 'JAGS' model
`JAGS_bridgesampling_posterior()`	Prepare 'JAGS' posterior for 'bridgesampling'
`JAGS_check_and_list_fit_settings()` `JAGS_check_and_list_autofit_settings()`	Check and list 'JAGS' fitting settings
`JAGS_check_convergence()`	Assess convergence of a runjags model
`JAGS_diagnostics()` `JAGS_diagnostics_density()` `JAGS_diagnostics_trace()` `JAGS_diagnostics_autocorrelation()`	Plot diagnostics of a 'JAGS' model
`JAGS_evaluate_formula()`	Evaluate JAGS formula using posterior samples
`JAGS_fit()` `JAGS_extend()`	Fits a 'JAGS' model
`JAGS_formula()`	Create JAGS formula syntax and data object
`JAGS_get_inits()`	Create initial values for 'JAGS' model
`JAGS_marglik_parameters()` `JAGS_marglik_parameters_formula()`	Extract parameters for 'JAGS' priors
`JAGS_marglik_priors()` `JAGS_marglik_priors_formula()`	Compute marginal likelihood for 'JAGS' priors
`JAGS_to_monitor()`	Create list of monitored parameters for 'JAGS' model
`Savage_Dickey_BF()`	Compute Savage-Dickey inclusion Bayes factors
`add_column()`	Adds column to BayesTools table
`bridgesampling_object()`	Create a 'bridgesampling' object
`check_bool()` `check_char()` `check_real()` `check_int()` `check_list()`	Check input
`contr.independent()`	Independent contrast matrix
`contr.meandif()`	Mean difference contrast matrix
`contr.orthonormal()`	Orthornomal contrast matrix
`density(<prior>)`	Prior density
`compute_inference()` `ensemble_inference()` `models_inference()`	Compute posterior probabilities and inclusion Bayes factors
`format_BF()`	Format Bayes factor
`geom_prior()`	Add prior object to a ggplot
`geom_prior_list()`	Add list of prior objects to a plot
`inclusion_BF()`	Compute inclusion Bayes factors
`interpret()`	Interpret ensemble inference and estimates
`is.prior()` `is.prior.point()` `is.prior.none()` `is.prior.simple()` `is.prior.discrete()` `is.prior.vector()` `is.prior.PET()` `is.prior.PEESE()` `is.prior.weightfunction()` `is.prior.factor()` `is.prior.orthonormal()` `is.prior.treatment()` `is.prior.independent()` `is.prior.spike_and_slab()` `is.prior.meandif()`	Reports whether x is a a prior object
`kitchen_rolls`	Kitchen Rolls data from Wagenmakers et al. (2015) replication study.
`lines(<prior>)`	Add prior object to a plot
`lines_prior_list()`	Add list of prior objects to a plot
`marginal_inference()`	Model-average marginal posterior distributions and marginal Bayes factors
`marginal_posterior()`	Model-average marginal posterior distributions
`mean(<prior>)`	Prior mean
`mix_posteriors()`	Model-average posterior distributions
`dmpoint()` `rmpoint()` `pmpoint()` `qmpoint()`	Multivariate point mass distribution
`format_parameter_names()` `JAGS_parameter_names()`	Clean parameter names from JAGS
`plot(<prior>)`	Plots a prior object
`plot_marginal()`	Plot samples from the marginal posterior distributions
`plot_models()`	Plot estimates from models
`plot_posterior()`	Plot samples from the mixed posterior distributions
`plot_prior_list()`	Plot a list of prior distributions
`dpoint()` `rpoint()` `ppoint()` `qpoint()`	Point mass distribution
`print(<BayesTools_table>)`	Print a BayesTools table
`print(<prior>)`	Prints a prior object
`prior()` `prior_none()`	Creates a prior distribution
`prior_PET()` `prior_PEESE()`	Creates a prior distribution for PET or PEESE models
`prior_factor()`	Creates a prior distribution for factors
`rng(<prior>)` `cdf(<prior>)` `ccdf(<prior>)` `lpdf(<prior>)` `pdf(<prior>)` `quant(<prior>)` `mcdf(<prior>)` `mccdf(<prior>)` `mlpdf(<prior>)` `mpdf(<prior>)` `mquant(<prior>)`	Elementary prior related functions
`rng()` `cdf()` `ccdf()` `quant()` `lpdf()` `pdf()` `mcdf()` `mccdf()` `mquant()` `mlpdf()` `mpdf()`	Creates generics for common statistical functions
`prior_informed()`	Creates an informed prior distribution based on research
`prior_informed_medicine_names`	Names of medical subfields from the Cochrane database of systematic reviews
`prior_spike_and_slab()`	Creates a spike and slab prior distribution
`prior_weightfunction()`	Creates a prior distribution for a weight function
`range(<prior>)`	Prior range
`remove_column()`	Removes column to BayesTools table
`sd()`	Creates generic for sd function
`sd(<prior>)`	Prior sd
`transform_factor_samples()`	Transform factor posterior samples into differences from the mean
`transform_meandif_samples()`	Transform meandif posterior samples into differences from the mean
`transform_orthonormal_samples()`	Transform orthonomal posterior samples into differences from the mean
`var()`	Creates generic for var function
`var(<prior>)`	Prior var
`mdone.sided()` `mdtwo.sided()` `mdone.sided_fixed()` `mdtwo.sided_fixed()` `rone.sided()` `rtwo.sided()` `rone.sided_fixed()` `rtwo.sided_fixed()` `mpone.sided()` `mptwo.sided()` `mpone.sided_fixed()` `mptwo.sided_fixed()` `mqone.sided()` `mqtwo.sided()` `mqone.sided_fixed()` `mqtwo.sided_fixed()`	Weight functions
`weightfunctions_mapping()`	Create coefficient mapping between multiple weightfunctions

Reference

All functions