exdqlm - Extended Dynamic Quantile Linear Models

Bayesian quantile-regression routines for dynamic state-space models and static regression under the extended asymmetric Laplace (exAL) error distribution. The dynamic state-space models are extended dynamic quantile linear models (exDQLMs). The package combines dynamic exDQLM inference via Laplace-delta variational Bayes (LDVB), Markov chain Monte Carlo (MCMC), and legacy importance-sampling variational Bayes (ISVB) with static exAL regression via LDVB and MCMC, reduced asymmetric Laplace/dynamic quantile linear model (AL/DQLM) paths through fixed skewness, component builders for trend/seasonality/regression blocks, static shrinkage priors including ridge, regularized horseshoe, and 'rhs_ns', evidence lower bound (ELBO) diagnostics, optional C++ accelerators, and posterior predictive synthesis across separately fitted quantiles through 'quantileSynthesis()'. Dynamic exDQLM methods are described in Barata et al. (2020) <doi:10.1214/21-AOAS1497>.