Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.

Provides functions for Maximum Likelihood
             Estimation, Markov Chain Monte Carlo, finding confidence
             intervals.  The implementation is heavily based on the
             original Fortran source code translated to R.

Performs Bayesian posterior inference for heteroskedastic Gaussian processes.
    Models are trained through MCMC including elliptical slice sampling (ESS) of 
    latent noise processes and Metropolis-Hastings sampling of 
    kernel hyperparameters. Replicates are handled efficientyly through a
    Woodbury formulation of the joint likelihood for the mean and noise process 
    (Binois, M., Gramacy, R., Ludkovski, M. (2018) <doi:10.1080/10618600.2018.1458625>)
    For large data, Vecchia-approximation for faster 
    computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R.,
    (2023), <doi:10.1080/10618600.2022.2129662>). Incorporates 'OpenMP' and 
    SNOW parallelization and utilizes 'C'/'C++' under the hood.

Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

Provides functions for the evaluation of basket
    trial designs with binary endpoints. Operating characteristics of a
    basket trial design are assessed by simulating trial data according to
    scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
    assessing decision probabilities on stratum and trial-level based on Go / No-go decision making.
    The package is build for high flexibility regarding decision rules,
    number of interim analyses, number of strata, and recruitment.
    The BHMs proposed by
    Berry et al. (2013) <doi:10.1177/1740774513497539>
    and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
    as well as a model that combines both approaches are implemented.
    Functions are provided to implement Bayesian decision rules as for example
    proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
    In addition, posterior point estimates (mean/median) and credible intervals
    for response rates and some model parameters can be calculated.
    For simulated trial data, bias and mean squared errors of posterior
    point estimates for response rates can be provided.