Bayesian Distribution Regression
Asymmetric simultaneous bayesian confidence bands
Asymptotic distribution regression
Bayesian distribution regression
Semi-asymptotic bayesian distribution
Counterfactual bayesian distribution regression
Semi-asymptotic counterfactual distribution
Binary glm object at several threshold values
The distribution of mean fitted logit probabilities
Fitted logit probabilities
Independence Metropolis-Hastings Algorithm
Indicator function
Joint asymptotic mutivariate density of parameters
Montiel Olea and Plagborg-Moller (2018) confidence bands
Laplace approximation of posterior to normal
Laplace approximation of posterior to normal
Logit likelihood function
Logit function
Parallel compute bayesian distribution regression
Parallel compute
Posterior distribution
Normal Prior distribution
Uniform Prior distribution
Quantile conversion of a bayesian distribution matrix
Random Walk Metropolis-Hastings Algorithm
Symmetric simultaneous bayesian confidence bands
Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.