Regression Models for Bounded Continuous and Discrete Responses
Convergence diagnostics
Convergence plots
Draw density plots
Probability density function of the beta distribution
Probability mass function of the beta-binomial distribution
Probability density function of the flexible beta distribution
Probability mass function of the flexible beta-binomial distribution
Probability density function of the variance-inflated beta distributio...
extract.pars
The `FlexReg' package.
Flexible Regression Models for Bounded Continuous Responses
Flexible Regression Models for Bounded Discrete Responses
mu.chain.nd
newdata.adjust
phi.chain.nd
Plot Method for flexreg Objects
Plot Method for flexreg_postpred objects
Posterior Predictive Method for flexreg objects
posterior_predict
Predict Method for flexreg Objects
predict_lambda.chain
predict_link
predict_mu.chain
predict_over
predict_precision
predict_q.chain
predict_response
predict_variance
Print Methods for flexreg Objects
q0.chain.nd
q01.chain.nd
q1.chain.nd
Bayesian R-squared for flexreg Objects
rate_plot
Random generator from the beta distribution
Random generator from the beta-binomial distribution
Residuals Method for flexreg Objects
Random generator from the flexible beta distribution
Random generator from the flexible beta-binomial distribution
Random generation from the variance-inflated beta distribution
Methods for flexreg Objects
Summary Method for flexreg_postpred objects
theta.chain.nd
var.fun
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.