Adaptive Bayesian Quantile Regression for Count Data // Implements Bayesian quantile regression for count data using the jittering technique for discrete data smoothing and an asymmetric Laplace distribution likelihood. Supports adaptive variable selection via a random-bridge penalty with a beta prior on the power parameter, as well as fixed-bridge and Lasso penalties. Utilizes Markov chain Monte Carlo with Gibbs sampling and adaptive Metropolis-Hastings algorithms for posterior inference, provides Gelman-Rubin convergence diagnostics, and predicts conditional quantiles for count responses. Methodology and applications are based on the following key references: Luo, Zhou, Hu, and Li (2026, Journal of Mathematics, 2026:1543166, doi:10.1155/jom/1543166), Koenker and Bassett (1978, Econometrica, 46, 33-50, doi:10.2307/1913643), Machado and Santos Silva (2005, Journal of the American Statistical Association, 100, 1226-1237, doi:10.1198/016214505000000330), Yu and Moyeed (2001, Statistics and Probability Letters, 54, 437-447, doi:10.1016/S0167-7152(01)00124-9), Polson, Scott, and Windle (2014, Journal of the Royal Statistical Society Series B, 76, 713-733, doi:10.1111/rssb.12042), Park and Casella (2008, Journal of the American Statistical Association, 103, 681-686, doi:10.1198/016214508000000337), and Roberts and Rosenthal (2009, Journal of Computational and Graphical Statistics, 18, 349-367, doi:10.1198/jcgs.2009.06134).