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>.

Make your 'shiny' application as executable program. 
    Users do not need to install 'R' and 'shiny' on their system.

Provides tools to automate external pharmacokinetic model evaluation workflows, including Bayesian forecasting, predictive performance metrics, diagnostic plotting, and automated reporting.

Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>.