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 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. The 40th Conference on Uncertainty in Artificial Intelligence, accepted).

Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <doi:10.48550/arXiv.1611.06649>.