Package index • bayestestR

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)

p_to_bf(): Convert p-values to (pseudo) Bayes Factors

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() as.logical(<bayesfactor_restricted>) as.matrix(<bayesfactor_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.

p_to_bf(): Convert p-values to (pseudo) Bayes Factors

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

distribution() distribution_custom() distribution_beta() distribution_binomial() distribution_binom() distribution_cauchy() distribution_chisquared() distribution_chisq() distribution_gamma() distribution_mixture_normal() distribution_normal() distribution_gaussian() distribution_nbinom() distribution_poisson() distribution_student() distribution_t() distribution_student_t() distribution_tweedie() distribution_uniform(): Empirical Distributions

Utilities

display(<describe_posterior>) print(<describe_posterior>) print_html(<describe_posterior>) print_md(<describe_posterior>): Print tables in different output formats

mediation(): Summary of Bayesian multivariate-response mediation-models

convert_bayesian_as_frequentist() bayesian_as_frequentist(): Convert (refit) a Bayesian model to frequentist

contr.equalprior() contr.equalprior_pairs() contr.equalprior_deviations(): Contrast Matrices for Equal Marginal Priors in 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)

disgust: Moral Disgust Judgment