Boost provides free peer-reviewed portable C++ source 
 libraries.  A large part of Boost is provided as C++ template code
 which is resolved entirely at compile-time without linking.  This 
 package aims to provide the most useful subset of Boost libraries 
 for template use among CRAN packages. By placing these libraries in 
 this package, we offer a more efficient distribution system for CRAN 
 as replication of this code in the sources of other packages is 
 avoided. As of release 1.84.0-0, the following Boost libraries are
 included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast'
 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config'
 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception'
 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap'
 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams'
 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision'
 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process'
 'propery_tree' 'qvm' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort'
 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'url' 'utility'
 'uuid'.

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.