Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.

Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models
             for censored data. Those can be not only right-censored but also interval-censored,
	     doubly-interval-censored or misclassified interval-censored. The methods implemented in the package
	     have been published in Komárek and Lesaffre (2006, Stat. Modelling) <doi:10.1191/1471082X06st107oa>,
	     Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) <doi:10.1002/sim.3083>, Komárek and Lesaffre (2007, Stat. Sinica) <https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, Komárek and Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>,
	     García-Zattera, Jara and Komárek (2016, Biometrics) <doi:10.1111/biom.12424>.

Provides Bayesian surveillance methods for prospective
    monitoring of healthcare performance, patient safety, and clinical
    quality indicators. The package implements beta-binomial monitoring
    for binary outcomes, gamma-Poisson monitoring for count outcomes,
    posterior predictive alert probabilities, Bayesian early-warning
    signal detection, risk-adjusted surveillance, simulation tools,
    decision-support methods, and graphical summaries. These methods
    support continuous performance monitoring and timely detection of
    adverse trends in healthcare systems. The methodology is motivated
    by established risk-adjusted monitoring, sequential surveillance,
    and healthcare quality-improvement frameworks
    <doi:10.1093/biostatistics/1.4.441>,
    <doi:10.1002/sim.1546>,
    <doi:10.1136/bmjqs.2008.031831>, and
    <doi:10.1136/bmjqs-2016-005526>.