Regression Models for Bounded Continuous and Discrete Responses
The Mean-Precision Parameterized Beta Distribution
The Beta-Binomial Distribution
internal function
Convergence diagnostics
internal function
Convergence plots
Draw density plots
extract.pars
The Flexible Beta Distribution
The Flexible Beta-Binomial Distribution
internal function
internal function
Flexible Regression Models for Bounded Discrete Responses
The 'FlexReg' package.
Flexible Regression Models for Bounded Continuous Responses
Construct Design Matrices for flexreg Objects
Construct Design Matrices for flexreg Objects
mu.chain.nd
newdata.adjust
phi.chain.nd
Plot Method for flexreg_postpred objects
Plot Method for flexreg Objects
Posterior Predictive Method for flexreg objects
posterior_predict
predict_lambda.chain
predict_link
predict_mu.chain
predict_over
predict_precision
predict_q.chain
predict_response
predict_variance
Predict Method for flexreg Objects
Print Methods for flexreg Objects
q0.chain.nd
q01.chain.nd
q1.chain.nd
Bayesian R-squared for flexreg Objects
internal function
rate_plot
Residuals Method for flexreg Objects
Summary Method for flexreg_postpred objects
Methods for flexreg Objects
theta.chain.nd
var.fun
The Variance-Inflated Beta Distribution
internal function
WAIC and LOO
Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) <doi:10.1002/sim.8406>) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided.