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., 2022) <doi:10.48550/arXiv.2207.09322>, Bottom-Up Importance Sampling (Zambon et al., 2022) <doi:10.48550/arXiv.2210.02286>.

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) <arXiv:1611.06649>.

Functionality for reliability estimates. For 'unidimensional' tests:
    Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
    bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. 
    For multidimensional tests: omega_t (total) and omega_h (hierarchical). 
    The results include confidence and credible intervals, the 
    probability of a coefficient being larger than a cutoff, 
    and a check for the factor models, necessary for the omega coefficients. 
    The method for the Bayesian 'unidimensional' estimates, except for omega_u, 
    is sampling from the posterior inverse 'Wishart' for the 
    covariance matrix based measures (see 'Murphy', 2007, 
    <https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. 
    The Bayesian omegas (u, t, and h) are obtained by 
    'Gibbs' sampling from the conditional posterior distributions of 
    (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, 
    (4) the correlated factor model
    ('Lee', 2007, <doi:10.1002/9780470024737>).