A Bayesian factor-analytic framework for Q methodology. Fits a
    low-rank factor model to Q-sort data with a Student-t likelihood and a
    hierarchical normal prior on loadings, samples the posterior with Stan,
    resolves rotational ambiguity via the MatchAlign post-processing of
    Poworoznek et al. (2025) <doi:10.1214/25-BA1544>, and returns posterior
    summaries including credible intervals for loadings and factor scores,
    probabilistic dominant-factor membership, distinguishing and consensus
    statements, and PSIS-LOO-based factor enumeration following Vehtari et
    al. (2017) <doi:10.1007/s11222-016-9696-4> with the Sivula et al. (2025)
    <doi:10.1214/25-BA1569> parsimony rule.

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).

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>,
             analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025)
             <doi: 10.48550/arXiv.2506.19554>.