Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

Bayesian analysis for exponential random graph models using
    advanced computational algorithms. More information can be found at:
    <https://acaimo.github.io/Bergm/>.

Bayesian analysis for stochastic extensions of non-linear
    dynamic systems using advanced computational algorithms. Described in Bouranis, L., 
    Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) <arXiv:2211.15229>.

Draw horizontal histograms, color scattered points by 3rd dimension,
    enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, 
    fit multiple functions.

Calculates several entropy metrics for spatial data 
  inspired by Boltzmann's entropy formula.
  It includes metrics introduced by Cushman for landscape mosaics 
  (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), 
  and landscape gradients and point patterns
  (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for 
  landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>);
  and by Gao et al. for landscape gradients
  (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.