Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for proportional hazards models
  with piecewise constant hazard. The methodology and examples of 
  applying the package are detailed in <doi:10.48550/arXiv.2404.05118>.
  The Bayesian clinical trial design methodology is described in 
  Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, 
  and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. 
  The proportional hazards model with piecewise constant hazard is detailed in 
  Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.

Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms.

Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.

Provides Bayesian quantile regression models for complex survey data 
    under informative sampling using survey-weighted estimators. Both single- and 
    multiple-output models are supported. To accelerate computation, all algorithms 
    are implemented in 'C++' using 'Rcpp', 'RcppArmadillo', and 'RcppEigen', and 
    are called from 'R'. See Nascimento and Gonçalves (2024) <doi:10.1093/jssam/smae015> 
    and Nascimento and Gonçalves (2025, in press) <https://academic.oup.com/jssam>.

Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. 
             The available methods include analytical Gaussian reconciliation (Corani et al., 2021) 
             <doi:10.1007/978-3-030-67664-3_13>, 
             MCMC reconciliation of count time series (Corani et al., 2024) 
             <doi:10.1016/j.ijforecast.2023.04.003>, 
             Bottom-Up Importance Sampling (Zambon et al., 2024) 
             <doi:10.1007/s11222-023-10343-y>,
             methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond)
             (Zambon et al., 2024) <https://proceedings.mlr.press/v244/zambon24a.html>.