Package index • BayesTools

All functions
`add_column()`	Adds column to BayesTools table
`as_marginal_inference()`	Model-average marginal posterior distributions and marginal Bayes factors based on BayesTools JAGS model via `marginal_inference`
`as_mixed_posteriors()`	Export BayesTools JAGS model posterior distribution as model-average posterior distributions via `mix_posteriors`
`BayesTools` `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
`BayesTools_load_JAGS_module()`	Load the BayesTools JAGS module
`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
`BF_carrier_drop()`	Remove the carrier attributes of a Bayes factor
`bridgesampling_object()`	Create a BayesTools marginal-likelihood object
`check_bool()` `check_char()` `check_real()` `check_int()` `check_list()`	Check input
`contr.orthonormal()` `contr.meandif()` `contr.independent()` `contr.ordered_cumulative()` `contr.ordered_cumulative_levels()`	BayesTools Contrast Matrices
`density(<prior>)`	Prior density
`compute_inference()` `ensemble_inference()` `models_inference()`	Compute posterior probabilities and inclusion Bayes factors
`fit_backend_fingerprint()`	Fingerprint the loaded BayesTools fitting backend
`format_BF()`	Format Bayes factor
`format_BF_from_log()`	Format Bayes factors from natural-log values
`formula_add_intercept()`	Add an Intercept to a Formula
`geom_prior()`	Add prior object to a ggplot
`geom_prior_list()`	Add list of prior objects to a plot
`harrell_davis_quantile()`	Harrell-Davis quantiles of posterior draws
`hypothesis_parse()` `hypothesis_ast_schema()` `hypothesis_render()` `hypothesis_symbols()` `hypothesis_rewrite()` `hypothesis_resolve()`	Versioned hypothesis syntax trees
`hypothesis_BF()` `as.data.frame(<BayesTools_hypothesis_BF>)`	Hypothesis Bayes Factors
`hypothesis_BF_model()`	Hypothesis Bayes factors of a fitted model
`hypothesis_cache_clear()`	Drop the caches of the hypothesis engine
`hypothesis_density_backend()` `register_hypothesis_density_backend()` `unregister_hypothesis_density_backend()` `hypothesis_density_backends()`	Density backends of fit-level hypothesis Bayes factors
`hypothesis_linear_target()`	Compile a hypothesis linear in the levels of one parameter
`hypothesis_linear_target_side()`	Linear form of one side of a hypothesis statement
`hypothesis_parse_point_reference()` `hypothesis_parse_level_reference()` `hypothesis_normalize_level_references()`	Hypothesis parser helpers
`hypothesis_provider()`	Hypothesis provider of a fitted model
`hypothesis_quantities()`	Available Hypothesis Quantities
`hypothesis_stop()`	Stop with a refusal of a hypothesis
`hypothesis_alias_label()` `hypothesis_unique_named_warnings()`	Helpers of hypothesis tables
`inclusion_BF()`	Compute inclusion Bayes factors
`interpret()` `interpret2()`	Interpret ensemble inference and estimates
`interpret_records()` `interpret_tables()`	Normalize structured interpretation sources
`invert_BF_bound_operator()`	Invert the orientation of Bayes factor bound operators
`is.prior()` `is.prior.point()` `is.prior.none()` `is.prior.simple()` `is.prior.discrete()` `is.prior.vector()` `is.prior.simplex()` `is.prior.PET()` `is.prior.PEESE()` `is.prior.weightfunction()` `is.prior.factor()` `is.prior.ordered()` `is.prior.orthonormal()` `is.prior.treatment()` `is.prior.independent()` `is.prior.spike_and_slab()` `is.prior.meandif()` `is.prior.mixture()`	Reports whether x is a a prior object
`is_prior_bias()`	Reports whether x is a composed publication-bias prior
`is_prior_phacking()`	Reports whether x is a p-hacking prior
`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_deterministic_nodes()` `JAGS_evaluate_deterministic()` `JAGS_deterministic_evaluator()`	Generated deterministic nodes
`JAGS_diagnostics()` `JAGS_diagnostics_density()` `JAGS_diagnostics_trace()` `JAGS_diagnostics_autocorrelation()`	Plot diagnostics of a 'JAGS' model
`JAGS_draw_geometry()` `JAGS_draw_geometry_schema()` `JAGS_materialize_draws()` `JAGS_with_draws()` `as.mcmc.list(<BayesTools_draws_view>)` `as.mcmc(<BayesTools_draws_view>)` `print(<BayesTools_draws_view>)`	JAGS draw geometry and deterministic materialization
`JAGS_evaluate_formula()`	Evaluate JAGS formula using posterior samples
`JAGS_fit()` `JAGS_extend()`	Fits a 'JAGS' model
`JAGS_formula_name_map()` `JAGS_fit_contract()` `JAGS_validate_fit_contract()` `JAGS_fit_contract_schema()`	JAGS formula name map and fitted-object contract
`JAGS_formula()`	Create JAGS formula syntax and data object
`JAGS_formula_coefficient_route()`	Original-scale route of a formula coefficient
`JAGS_formula_internal_coordinate_priors()` `JAGS_formula_coefficient_transform()` `JAGS_formula_coefficient_transform_schema()` `JAGS_formula_prior_density()`	Formula coefficient transformations and induced prior densities
`JAGS_formula_coordinate_dependencies()`	Fitted-formula coordinate dependencies
`JAGS_formula_design()`	Extract Fitted JAGS Formula Design Metadata
`JAGS_formula_draws()`	Formula designs for draws without a fit
`JAGS_formula_point_finalize()` `JAGS_formula_scale_finalize()`	Finalize formula designs built outside JAGS_fit()
`JAGS_formula_predictor_basis()`	Exact fitted-formula predictor update basis
`JAGS_formula_random_marginal_covariance()`	Compile formula random effects as marginal JAGS covariance nodes
`JAGS_get_inits()`	Create initial values for 'JAGS' model
`JAGS_lkj_corr_cholesky()`	Generate a JAGS LKJ-Cholesky correlation module
`JAGS_marglik_parameters_formula()` `JAGS_marglik_parameters()`	Extract parameters for 'JAGS' priors
`JAGS_marglik_priors()` `JAGS_marglik_priors_rows()` `JAGS_marglik_priors_rows_evaluator()` `JAGS_marglik_priors_formula()`	Compute marginal likelihood for 'JAGS' priors
`JAGS_ordered_density_kernel()`	Batched numerical density kernel for ordered primitives
`JAGS_ordered_parameter_spec()`	Fitted ordered parameter specifications
`JAGS_package_builds()`	Build fingerprints of loaded packages
`JAGS_predict_formula()`	Formula prediction with explicit random-effect target
`JAGS_runtime_cluster()` `JAGS_runtime_cluster_stop()`	Worker clusters with the running package builds
`JAGS_to_monitor()`	Create list of monitored parameters for 'JAGS' model
`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
`parameter_catalog()` `parameter_catalog_schema()` `parameter_catalog_extend()` `parameter_catalog_resolve()` `parameter_draws()` `parameter_prior_density()` `parameter_transform()` `parameter_transform_forward()` `parameter_transform_inverse()` `parameter_transform_jacobian()`	Semantic parameter catalogs and deferred draw extraction
`parameter_gate_states()`	Per-draw gate and point states of a catalog quantity
`parameter_labels()`	Parameter labels
`parameter_map()` `parameter_coordinates()` `parameter_coordinates_schema()` `parameter_map_schema()`	Fitted parameter map and coordinate view
`parameter_map_cache()`	Cache a value derived from a fitted parameter map
`parameter_mixed_posterior()`	Mixed posterior of one catalog quantity
`format_parameter_names()` `JAGS_parameter_names()` `JAGS_regex_escape()` `JAGS_indexed_parameter_columns()` `JAGS_indexed_parameter_matrix()` `JAGS_indexed_parameter_vector()`	JAGS parameter-name helpers
`parameter_source()` `random_sd_source()`	External JAGS parameter source
`phack_pi_null()` `phack_alpha_from_pi_null()` `phack_backend_constants()`	P-hacking calibration helpers
`plot(<prior>)`	Plots a prior object
`plot(<prior_linear_density>)`	Plot a prior linear density
`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
`plot_transformed_prior()`	Plot Transformed Prior Densities
`dpoint()` `rpoint()` `ppoint()` `qpoint()`	Point mass distribution
`posterior_atoms_free()`	Whether posterior draws are declared atom-free
`posterior_atom_attribute()`	Declare posterior point-mass metadata
`posterior_density_attribute()` `posterior_ordinate_attribute()` `posterior_ordinate_append()` `posterior_ordinate_supports_bf()` `posterior_ordinate_has_value()`	Posterior density and ordinate attributes
`posterior_density_method_match()` `posterior_density_method_uses_precomputed()`	Posterior density method helpers
`posterior_formula_measure_check()` `posterior_linear_weight_space()` `posterior_fitted_linear_weights()`	Metadata checks of the linear targets of posterior draws
`posterior_log_ordinate_attribute()`	Posterior ordinate attributes from log heights
`posterior_measure_check()`	Check that a measure of posterior draws is available
`posterior_metadata()` `posterior_metadata<-`()	Metadata of BayesTools posterior draws
`posterior_support_attribute()`	Posterior support metadata
`posterior_transform()`	Transform posterior draws with their metadata
`print(<BayesTools_table>)`	Print a BayesTools table
`print(<prior>)`	Prints a prior object
`prior()` `prior_none()`	Creates a prior distribution
`prior_bias()`	Creates a composed publication-bias prior
`prior_density_has_provenance()`	Whether a prior density has deterministic provenance
`prior_density_ordinate()`	Classify a prior-density ordinate
`prior_factor()`	Creates a prior distribution for factors
`prior_factor_levels()`	Levels of a factor prior distribution
`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_mixture()`	Creates a mixture of prior distributions
`prior_ordered()`	Creates a prior distribution for ordered factor effects
`prior_ordered_total_name()`	Name of the total node of an ordered prior
`prior_ordinate_status()`	Point-hypothesis eligibility of prior ordinates
`prior_phacking()`	Creates a prior distribution for a p-hacking selection kernel
`prior_PET()` `prior_PEESE()`	Creates a prior distribution for PET or PEESE models
`prior_random()` `random_block()` `random_variance_allocation()` `allocation_ref()` `random_covariance()` `prior_lkj()` `random_monitor()` `random_new_levels()`	Random-effect prior specification
`prior_spike_and_slab()`	Creates a spike and slab prior distribution
`prior_weightfunction()` `wf_cumulative()` `wf_fixed()` `wf_independent()`	Creates a prior distribution for a weight function
`random_effects_compile()`	Random-effect compilation policy
`random_effects_correlation_draws()`	Scalar-structured random-effect correlation draws
`random_effects_dependency_matrix()`	Structural row dependencies of formula random effects
`random_effects_formula()`	Normalize random-effect formula input
`random_effects_level_roles()`	Grouping-level roles of formula random effects
`random_effects_marginal_diagonal_factor()`	Diagonal-plus-factor random-effect marginal representation
`random_effects_marginal_factor_diagonal()`	Random-effect marginal covariance diagonals from factor states
`random_effects_marginal_factor_product()`	Multiply random-effect marginal covariance factors by row vectors
`random_effects_marginal_factor_states()`	Random-effect marginal covariance factor states
`random_effects_marginal_factor_vcov()`	Materialize random-effect marginal covariance factors
`random_effects_marginal_update_grid()`	Random-effect marginal covariance update grid
`random_effects_marginal_update_plan()`	Random-effect marginal covariance update plan
`random_effects_marginal_variance_factors()`	Random-effect marginal variance factors
`random_effects_marginal_vcov()`	Random-effect marginal variance-covariance samples
`random_effects_source_roles()`	Structural source roles of formula random effects
`random_effects_summary_posterior()`	Extract random-effect summary posterior distributions
`random_group_component_names()` `random_group_prediction_keys()`	Grouping variables and group keys of a compiled random-effect term
`random_group_covariance()`	Known group covariance for random effects
`range(<prior>)`	Prior range
`remove_column()`	Removes column to BayesTools table
`Savage_Dickey_BF()`	Compute Savage-Dickey inclusion Bayes factors
`sd(<prior>)`	Prior sd
`sd()`	Creates generic for sd function
`selection_backend_spec()`	Compile selection priors for backend consumers
`selection_context_validate()` `selection_context_subset_rows()` `selection_context_subset_observations()` `selection_native_static_args()` `selection_native_kernel_args()` `selection_row_arg()`	Selection context helpers
`selection_event_support()`	Check Gaussian selection-event support
`selection_model()` `selection_model_spec()` `check_selection_model()` `print(<selection_model>)`	Specifies source conditioning in a selection model
`selection_qmc_design()`	Fixed randomized quasi-Monte Carlo design
`simplify_as_mixed_posterior_bias()`	Scalar PET, PEESE or omega posterior from a bias mixed posterior
`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
`transform_prior_samples()`	Transform prior samples to original scale
`transform_scale_samples()`	Transform standardized posterior samples back to original scale
`update(<BayesTools_table>)`	Updates BayesTools table
`var(<prior>)`	Prior var
`var()`	Creates generic for var function
`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