Package index • hurdlebb

Package

hurdlebb hurdlebb-package: hurdlebb: Hurdle Beta-Binomial Models for Bounded Discrete Proportions

Main fitting function and formula specification

Build and validate Stan-ready data structures

Customize or inspect model priors

hbb_prior(): Specify Priors for a Hurdle Beta-Binomial Model
default_prior(): Default Prior Specification for Hurdle Beta-Binomial Models

Beta-Binomial, zero-truncated, and hurdle distribution functions

dbetabinom(): Probability mass function of the Beta-Binomial distribution
pbetabinom(): Cumulative distribution function of the Beta-Binomial distribution
rbetabinom(): Random generation from the Beta-Binomial distribution
dztbetabinom(): Probability mass function of the Zero-Truncated Beta-Binomial
rztbetabinom(): Random generation from the Zero-Truncated Beta-Binomial
dhurdle_betabinom(): Probability mass function of the Hurdle Beta-Binomial
rhurdle_betabinom(): Random generation from the Hurdle Beta-Binomial
compute_p0(): Compute P(Y = 0) under the Beta-Binomial distribution
compute_ztbb_mean(): Conditional mean of the zero-truncated Beta-Binomial
hurdle_mean(): Expected value under the Hurdle Beta-Binomial
hurdle_variance(): Variance under the Hurdle Beta-Binomial

Sandwich variance and Cholesky posterior recalibration for survey data

sandwich_variance(): Cluster-Robust Sandwich Variance Estimator
cholesky_correct(): Cholesky Posterior Recalibration for Survey-Weighted Inference
compute_wald_ci(): Wald Confidence Intervals from Sandwich Variance

Average marginal effect decomposition into extensive and intensive margins

ame(): Average Marginal Effects Decomposition for Hurdle Beta-Binomial Models
ame_decomposition(): Extract the AME Decomposition Table

LOO cross-validation and posterior predictive checks

loo(<hbb_fit>): Leave-One-Out Cross-Validation for Hurdle Beta-Binomial Models
hbb_loo_compare(): Compare Hurdle Beta-Binomial Models via LOO-CV
ppc(): Posterior Predictive Checks for Hurdle Beta-Binomial Models

Standard generic methods for hbb_fit objects

coef(<hbb_fit>): Extract Fixed-Effect Coefficients from an hbb_fit
vcov(<hbb_fit>): Variance-Covariance Matrix of Fixed Effects
fitted(<hbb_fit>): Fitted Values from an hbb_fit
residuals(<hbb_fit>): Residuals from an hbb_fit
predict(<hbb_fit>): Predictions from a Hurdle Beta-Binomial Model
summary(<hbb_fit>): Summary Method for hbb_fit Objects
nobs(<hbb_fit>): Number of Observations in an hbb_fit
plot(<hbb_fit>): Plot Diagnostics and Inferential Summaries for hbb_fit Objects
plot(<hbb_ppc>): Plot Method for hbb_ppc Objects
print(<hbb_fit>): Print Method for hbb_fit Objects
print(<hbb_sandwich>): Print Method for hbb_sandwich Objects
print(<hbb_cholesky>): Print Method for Cholesky-Corrected Posterior Objects
print(<hbb_ame>): Print Method for hbb_ame Objects
print(<hbb_loo_compare>): Print Method for hbb_loo_compare Objects
print(<hbb_ppc>): Print Method for hbb_ppc Objects
print(<summary.hbb_fit>): Print Method for summary.hbb_fit Objects

Compile and cache Stan models

Lower-level components for sandwich variance computation

compute_score_matrix(): Extract Posterior Mean Score Matrix from an hbb_fit
compute_H_obs(): Compute the Block-Diagonal Observed Information Matrix
compute_J_cluster(): Compute the Cluster-Robust Meat Matrix
compute_der(): Compute Design Effect Ratios from a Sandwich Variance Object

Built-in synthetic datasets based on NSECE 2019