BF_carrier_drop         Remove the carrier attributes of a Bayes factor
BayesTools              BayesTools
BayesTools_ensemble_tables
                        Create BayesTools ensemble summary tables
BayesTools_load_JAGS_module
                        Load the BayesTools JAGS module
BayesTools_model_tables
                        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     Check and list 'JAGS' fitting settings
JAGS_check_convergence
                        Assess convergence of a runjags model
JAGS_deterministic_nodes
                        Generated deterministic nodes
JAGS_diagnostics        Plot diagnostics of a 'JAGS' model
JAGS_draw_geometry      JAGS draw geometry and deterministic
                        materialization
JAGS_evaluate_formula   Evaluate JAGS formula using posterior samples
JAGS_fit                Fits a 'JAGS' model
JAGS_fit_contract       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_coefficient_transform
                        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
                        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
                        Extract parameters for 'JAGS' priors
JAGS_marglik_priors     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    Worker clusters with the running package builds
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
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'
bridgesampling_object   Create a BayesTools marginal-likelihood object
check_input             Check input
contr.BayesTools        BayesTools Contrast Matrices
density.prior           Prior density
ensemble_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_BF           Hypothesis Bayes Factors
hypothesis_BF_model     Hypothesis Bayes factors of a fitted model
hypothesis_alias_label
                        Helpers of hypothesis tables
hypothesis_ast          Versioned hypothesis syntax trees
hypothesis_cache_clear
                        Drop the caches of the hypothesis engine
hypothesis_density_backend
                        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 parser helpers
hypothesis_provider     Hypothesis provider of a fitted model
hypothesis_quantities   Available Hypothesis Quantities
hypothesis_stop         Stop with a refusal of a hypothesis
inclusion_BF            Compute inclusion Bayes factors
interpret               Interpret ensemble inference and estimates
interpret_records       Normalize structured interpretation sources
invert_BF_bound_operator
                        Invert the orientation of Bayes factor bound
                        operators
is.prior                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
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
mpoint                  Multivariate point mass distribution
parameter_catalog       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           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
parameter_names         JAGS parameter-name helpers
parameter_source        External JAGS parameter source
phack_pi_null           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
point                   Point mass distribution
posterior_atom_attribute
                        Declare posterior point-mass metadata
posterior_atoms_free    Whether posterior draws are declared atom-free
posterior_density_attribute
                        Posterior density and ordinate attributes
posterior_density_method_match
                        Posterior density method helpers
posterior_formula_measure_check
                        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      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                   Creates a prior distribution
prior_PP                Creates a prior distribution for PET or PEESE
                        models
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
prior_functions         Elementary prior related functions
prior_functions_methods
                        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_random            Random-effect prior specification
prior_spike_and_slab    Creates a spike and slab prior distribution
prior_weightfunction    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
                        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
sd                      Creates generic for sd function
sd.prior                Prior sd
selection_backend_spec
                        Compile selection priors for backend consumers
selection_context_validate
                        Selection context helpers
selection_event_support
                        Check Gaussian selection-event support
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                     Creates generic for var function
var.prior               Prior var
weightfunctions         Weight functions
weightfunctions_mapping
                        Create coefficient mapping between multiple
                        weightfunctions
