Provides helper functions to perform Bayesian model averaging
    using Markov chain Monte Carlo samples from separate models. Calculates
    weights and obtains draws from the model-averaged posterior for quantities
    of interest specified by the user. Weight calculations can be done using
    marginal likelihoods or log-predictive likelihoods as in Ando, T., & Tsay,
    R. (2010) <doi:10.1016/j.ijforecast.2009.08.001>.

Analyzing performances of cricketers and cricket teams
             based on 'yaml' match data from Cricsheet <https://cricsheet.org/>.

Dataset from the young elite swimmers study.

Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <arXiv:1910.04475>. Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.

For any spending function specified by the user, this 
    package provides corresponding boundaries for interim testing using
    the adaptively weighted log-rank test developed by Yang and Prentice
    (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). 
    The package uses a re-sampling method to obtain stopping boundaries 
    at the interim looks.The output consists of stopping boundaries 
    and observed values of the test statistics at the interim looks, 
    along with nominal p-values defined as the probability of the test 
    exceeding the specific observed test statistic value or critical 
    value, regardless of the test behavior at other looks. 
    The asymptotic validity of the stopping boundaries is established
    in Yang (2018 <doi:10.1002/sim.7958>).

Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.