Bayesian models to estimate causal effects of biological 
    treatments on time-to-event endpoints in clinical trials with principal
    strata defined by the occurrence of antidrug antibodies. 
    The methodology is based on Frangakis and Rubin (2002) 
    <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997)
    <doi:10.1214/aos/1034276631>, and here adapted to a specific 
    time-to-event setting.

A collection of functions that allow servers (specifically BPKP) to 
    summarize and verify shift earnings, identify composition of tips and hourly wage, and
    track performance through several metrics.

Fits Bayesian mixed-effects (multilevel) quantile regression
    models using the asymmetric Laplace working likelihood and Stan. Supports
    an 'lme4'-style formula interface with nested and crossed random effects,
    fitting one or several quantiles, post-hoc non-crossing rearrangement of
    fitted quantiles, and the Yang, Wang and He (2016) posterior-variance
    correction for valid frequentist inference from the (misspecified)
    asymmetric Laplace posterior.

Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models.
    Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>.
    Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
    Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>.
    Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
    Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.