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Posterior Description

describe_posterior()
Describe Posterior Distributions
describe_prior()
Describe Priors
sexit()
Sequential Effect eXistence and sIgnificance Testing (SEXIT)

Centrality and Uncertainty

as.numeric(<map_estimate>) as.numeric(<p_direction>) as.numeric(<p_map>) as.numeric(<p_significance>)
Convert to Numeric
map_estimate()
Maximum A Posteriori probability estimate (MAP)
point_estimate()
Point-estimates of posterior distributions
bci() bcai()
Bias Corrected and Accelerated Interval (BCa)
eti()
Equal-Tailed Interval (ETI)
hdi()
Highest Density Interval (HDI)
spi()
Shortest Probability Interval (SPI)
ci()
Confidence/Credible/Compatibility Interval (CI)

Effect Existence and Significance

Functions for Bayesian Inference

Posterior Based Methods

p_direction() pd()
Probability of Direction (pd)
p_map() p_pointnull()
Bayesian p-value based on the density at the Maximum A Posteriori (MAP)
p_rope()
Probability of being in the ROPE
p_significance()
Practical Significance (ps)
pd_to_p() p_to_pd() convert_p_to_pd() convert_pd_to_p()
Convert between Probability of Direction (pd) and p-value.
bayesfactor_parameters() bayesfactor_pointnull() bayesfactor_rope() bf_parameters() bf_pointnull() bf_rope()
Bayes Factors (BF) for a Single Parameter
rope()
Region of Practical Equivalence (ROPE)
rope_range()
Find Default Equivalence (ROPE) Region Bounds
equivalence_test()
Test for Practical Equivalence

Bayes factors

bayesfactor()
Bayes Factors (BF)
bayesfactor_inclusion() bf_inclusion()
Inclusion Bayes Factors for testing predictors across Bayesian models
bayesfactor_models() bf_models() update(<bayesfactor_models>) as.matrix(<bayesfactor_models>)
Bayes Factors (BF) for model comparison
bayesfactor_parameters() bayesfactor_pointnull() bayesfactor_rope() bf_parameters() bf_pointnull() bf_rope()
Bayes Factors (BF) for a Single Parameter
bayesfactor_restricted() bf_restricted()
Bayes Factors (BF) for Order Restricted Models
si()
Compute Support Intervals
weighted_posteriors()
Generate posterior distributions weighted across models
bic_to_bf()
Convert BIC indices to Bayes Factors via the BIC-approximation method.

Model Diagnostics

diagnostic_posterior()
Posteriors Sampling Diagnostic
sensitivity_to_prior()
Sensitivity to Prior
check_prior()
Check if Prior is Informative
simulate_correlation() simulate_ttest() simulate_difference()
Data Simulation
simulate_prior()
Returns Priors of a Model as Empirical Distributions
simulate_simpson()
Simpson's paradox dataset simulation
unupdate()
Un-update Bayesian models to their prior-to-data state
effective_sample()
Effective Sample Size (ESS)
mcse()
Monte-Carlo Standard Error (MCSE)

Density Estimation

estimate_density()
Density Estimation
density_at()
Density Probability at a Given Value
area_under_curve() auc()
Area under the Curve (AUC)
overlap()
Overlap Coefficient

Distributions

Utilities

mediation()
Summary of Bayesian multivariate-response mediation-models
convert_bayesian_as_frequentist() bayesian_as_frequentist()
Convert (refit) a Bayesian model to frequentist
contr.orthonorm()
Orthonormal Contrast Matrices for Bayesian Estimation
as.numeric(<map_estimate>) as.numeric(<p_direction>) as.numeric(<p_map>) as.numeric(<p_significance>)
Convert to Numeric
as.data.frame(<density>)
Coerce to a Data Frame
sexit_thresholds()
Find Effect Size Thresholds
reshape_iterations() reshape_draws()
Reshape estimations with multiple iterations (draws) to long format
diagnostic_draws()
Diagnostic values for each iteration
model_to_priors()
Convert model's posteriors to priors (EXPERIMENTAL)
cwi()
Curvewise Intervals (CWI)