R: Understand and Describe Bayesian Models and Posterior Distributions

# Understand and Describe Bayesian Models and Posterior Distributions

## Documentation for package ‘bayestestR’ version 0.16.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E H M O P R S U W

## -- A --

area_under_curve | Area under the Curve (AUC)
as.data.frame.density | Coerce to a Data Frame
as.logical.bayesfactor_restricted | Bayes Factors (BF) for Order Restricted Models
as.matrix.bayesfactor_models | Bayes Factors (BF) for model comparison
as.numeric.map_estimate | Convert to Numeric
as.numeric.p_direction | Convert to Numeric
as.numeric.p_map | Convert to Numeric
as.numeric.p_significance | Convert to Numeric
auc | Area under the Curve (AUC)

## -- B --

bayesfactor | Bayes Factors (BF)
bayesfactor_inclusion | Inclusion Bayes Factors for testing predictors across Bayesian models
bayesfactor_models | Bayes Factors (BF) for model comparison
bayesfactor_models.default | Bayes Factors (BF) for model comparison
bayesfactor_parameters | Bayes Factors (BF) for a Single Parameter
bayesfactor_parameters.data.frame | Bayes Factors (BF) for a Single Parameter
bayesfactor_parameters.numeric | Bayes Factors (BF) for a Single Parameter
bayesfactor_parameters.stanreg | Bayes Factors (BF) for a Single Parameter
bayesfactor_pointnull | Bayes Factors (BF) for a Single Parameter
bayesfactor_restricted | Bayes Factors (BF) for Order Restricted Models
bayesfactor_restricted.blavaan | Bayes Factors (BF) for Order Restricted Models
bayesfactor_restricted.brmsfit | Bayes Factors (BF) for Order Restricted Models
bayesfactor_restricted.data.frame | Bayes Factors (BF) for Order Restricted Models
bayesfactor_restricted.emmGrid | Bayes Factors (BF) for Order Restricted Models
bayesfactor_restricted.stanreg | Bayes Factors (BF) for Order Restricted Models
bayesfactor_rope | Bayes Factors (BF) for a Single Parameter
bayesian_as_frequentist | Convert (refit) a Bayesian model to frequentist
bcai | Bias Corrected and Accelerated Interval (BCa)
bci | Bias Corrected and Accelerated Interval (BCa)
bci.brmsfit | Bias Corrected and Accelerated Interval (BCa)
bci.data.frame | Bias Corrected and Accelerated Interval (BCa)
bci.get_predicted | Bias Corrected and Accelerated Interval (BCa)
bci.numeric | Bias Corrected and Accelerated Interval (BCa)
bf_inclusion | Inclusion Bayes Factors for testing predictors across Bayesian models
bf_models | Bayes Factors (BF) for model comparison
bf_parameters | Bayes Factors (BF) for a Single Parameter
bf_pointnull | Bayes Factors (BF) for a Single Parameter
bf_restricted | Bayes Factors (BF) for Order Restricted Models
bf_rope | Bayes Factors (BF) for a Single Parameter
bic_to_bf | Convert BIC indices to Bayes Factors via the BIC-approximation method.

## -- C --

check_prior | Check if Prior is Informative
check_prior.brmsfit | Check if Prior is Informative
ci | Confidence/Credible/Compatibility Interval (CI)
ci.brmsfit | Confidence/Credible/Compatibility Interval (CI)
ci.data.frame | Confidence/Credible/Compatibility Interval (CI)
ci.numeric | Confidence/Credible/Compatibility Interval (CI)
contr.bayes | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation
contr.equalprior | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation
contr.equalprior_deviations | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation
contr.equalprior_pairs | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation
contr.orthonorm | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation
convert_bayesian_as_frequentist | Convert (refit) a Bayesian model to frequentist
convert_pd_to_p | Convert between Probability of Direction (pd) and p-value.
convert_p_to_pd | Convert between Probability of Direction (pd) and p-value.

## -- D --

density_at | Density Probability at a Given Value
describe_posterior | Describe Posterior Distributions
describe_posterior.data.frame | Describe Posterior Distributions
describe_posterior.numeric | Describe Posterior Distributions
describe_posterior.stanreg | Describe Posterior Distributions
describe_prior | Describe Priors
describe_prior.brmsfit | Describe Priors
diagnostic_draws | Diagnostic values for each iteration
diagnostic_posterior | Posteriors Sampling Diagnostic
diagnostic_posterior.default | Posteriors Sampling Diagnostic
diagnostic_posterior.stanreg | Posteriors Sampling Diagnostic
disgust | Moral Disgust Judgment
distribution | Empirical Distributions
distribution_beta | Empirical Distributions
distribution_binom | Empirical Distributions
distribution_binomial | Empirical Distributions
distribution_cauchy | Empirical Distributions
distribution_chisq | Empirical Distributions
distribution_chisquared | Empirical Distributions
distribution_custom | Empirical Distributions
distribution_gamma | Empirical Distributions
distribution_gaussian | Empirical Distributions
distribution_mixture_normal | Empirical Distributions
distribution_nbinom | Empirical Distributions
distribution_normal | Empirical Distributions
distribution_poisson | Empirical Distributions
distribution_student | Empirical Distributions
distribution_student_t | Empirical Distributions
distribution_t | Empirical Distributions
distribution_tweedie | Empirical Distributions
distribution_uniform | Empirical Distributions

## -- E --

effective_sample | Effective Sample Size (ESS)
effective_sample.brmsfit | Effective Sample Size (ESS)
equivalence_test | Test for Practical Equivalence
equivalence_test.brmsfit | Test for Practical Equivalence
equivalence_test.data.frame | Test for Practical Equivalence
equivalence_test.default | Test for Practical Equivalence
estimate_density | Density Estimation
estimate_density.brmsfit | Density Estimation
estimate_density.data.frame | Density Estimation
eti | Equal-Tailed Interval (ETI)
eti.brmsfit | Equal-Tailed Interval (ETI)
eti.data.frame | Equal-Tailed Interval (ETI)
eti.get_predicted | Equal-Tailed Interval (ETI)
eti.numeric | Equal-Tailed Interval (ETI)

## -- H --

hdi | Highest Density Interval (HDI)
hdi.brmsfit | Highest Density Interval (HDI)
hdi.data.frame | Highest Density Interval (HDI)
hdi.get_predicted | Highest Density Interval (HDI)
hdi.numeric | Highest Density Interval (HDI)

## -- M --

map_estimate | Maximum A Posteriori probability estimate (MAP)
map_estimate.brmsfit | Maximum A Posteriori probability estimate (MAP)
map_estimate.data.frame | Maximum A Posteriori probability estimate (MAP)
map_estimate.get_predicted | Maximum A Posteriori probability estimate (MAP)
map_estimate.numeric | Maximum A Posteriori probability estimate (MAP)
mcse | Monte-Carlo Standard Error (MCSE)
mcse.stanreg | Monte-Carlo Standard Error (MCSE)
mediation | Summary of Bayesian multivariate-response mediation-models
mediation.brmsfit | Summary of Bayesian multivariate-response mediation-models
model_to_priors | Convert model's posteriors to priors (EXPERIMENTAL)

## -- O --

overlap | Overlap Coefficient

## -- P --

pd | Probability of Direction (pd)
pd_to_p | Convert between Probability of Direction (pd) and p-value.
pd_to_p.numeric | Convert between Probability of Direction (pd) and p-value.
point_estimate | Point-estimates of posterior distributions
point_estimate.brmsfit | Point-estimates of posterior distributions
point_estimate.data.frame | Point-estimates of posterior distributions
point_estimate.get_predicted | Point-estimates of posterior distributions
point_estimate.numeric | Point-estimates of posterior distributions
p_direction | Probability of Direction (pd)
p_direction.brmsfit | Probability of Direction (pd)
p_direction.data.frame | Probability of Direction (pd)
p_direction.get_predicted | Probability of Direction (pd)
p_direction.numeric | Probability of Direction (pd)
p_map | Bayesian p-value based on the density at the Maximum A Posteriori (MAP)
p_map.brmsfit | Bayesian p-value based on the density at the Maximum A Posteriori (MAP)
p_map.data.frame | Bayesian p-value based on the density at the Maximum A Posteriori (MAP)
p_map.get_predicted | Bayesian p-value based on the density at the Maximum A Posteriori (MAP)
p_map.numeric | Bayesian p-value based on the density at the Maximum A Posteriori (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_rope.brmsfit | Probability of being in the ROPE
p_rope.data.frame | Probability of being in the ROPE
p_rope.numeric | Probability of being in the ROPE
p_significance | Practical Significance (ps)
p_significance.brmsfit | Practical Significance (ps)
p_significance.data.frame | Practical Significance (ps)
p_significance.get_predicted | Practical Significance (ps)
p_significance.numeric | Practical Significance (ps)
p_to_bf | Convert p-values to (pseudo) Bayes Factors
p_to_bf.default | Convert p-values to (pseudo) Bayes Factors
p_to_bf.numeric | Convert p-values to (pseudo) Bayes Factors
p_to_pd | Convert between Probability of Direction (pd) and p-value.

## -- R --

reshape_draws | Reshape estimations with multiple iterations (draws) to long format
reshape_iterations | Reshape estimations with multiple iterations (draws) to long format
rnorm_perfect | Empirical Distributions
rope | Region of Practical Equivalence (ROPE)
rope.brmsfit | Region of Practical Equivalence (ROPE)
rope.data.frame | Region of Practical Equivalence (ROPE)
rope.numeric | Region of Practical Equivalence (ROPE)
rope_range | Find Default Equivalence (ROPE) Region Bounds
rope_range.default | Find Default Equivalence (ROPE) Region Bounds

## -- S --

sensitivity_to_prior | Sensitivity to Prior
sensitivity_to_prior.stanreg | Sensitivity to Prior
sexit | Sequential Effect eXistence and sIgnificance Testing (SEXIT)
sexit_thresholds | Find Effect Size Thresholds
si | Compute Support Intervals
si.data.frame | Compute Support Intervals
si.get_predicted | Compute Support Intervals
si.numeric | Compute Support Intervals
si.stanreg | Compute Support Intervals
simulate_correlation | Data Simulation
simulate_difference | Data Simulation
simulate_prior | Returns Priors of a Model as Empirical Distributions
simulate_prior.brmsfit | Returns Priors of a Model as Empirical Distributions
simulate_simpson | Simpson's paradox dataset simulation
simulate_ttest | Data Simulation
spi | Shortest Probability Interval (SPI)
spi.brmsfit | Shortest Probability Interval (SPI)
spi.data.frame | Shortest Probability Interval (SPI)
spi.get_predicted | Shortest Probability Interval (SPI)
spi.numeric | Shortest Probability Interval (SPI)

## -- U --

update.bayesfactor_models | Bayes Factors (BF) for model comparison

## -- W --

weighted_posteriors | Generate posterior distributions weighted across models
weighted_posteriors.BFBayesFactor | Generate posterior distributions weighted across models
weighted_posteriors.data.frame | Generate posterior distributions weighted across models
weighted_posteriors.stanreg | Generate posterior distributions weighted across models