Simulation of phenotype / genotype data under assortative mating. 
    Includes functions for generating Bahadur order-2 multivariate Bernoulli 
    variables with general and diagonal-plus-low-rank correlation structures. 
    Further details are provided in: Border and Malik (2022) 
    <doi:10.1101/2022.10.13.512132>.

An interface to the 'BaM' (Bayesian Modeling) engine, 
    a 'Fortran'-based executable aimed at estimating a model 
    with a Bayesian approach and using it for prediction,
    with a particular focus on uncertainty quantification.
    Classes are defined for the various building blocks of 
    'BaM' inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions).
    The typical usage is as follows:
    (1) specify the model to be estimated; 
    (2) specify the inference setting (dataset, parameters, error models...);
    (3) perform Bayesian-MCMC inference;
    (4) read, analyse and use MCMC samples;
    (5) perform prediction experiments.
    Technical details are available (in French) in 
    Renard (2017) <https://hal.science/hal-02606929v1>.
    Examples of applications include 
    Mansanarez et al. (2019) <doi:10.1029/2018WR023389>,
    Le Coz et al. (2021) <doi:10.1002/hyp.14169>,
    Perret et al. (2021) <doi:10.1029/2020WR027745>,
    Darienzo et al. (2021) <doi:10.1029/2020WR028607> and
    Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.

'BaseX' <https://basex.org> is a XML database engine and a compliant 'XQuery 3.1' processor with full support of 'W3C Update Facility'. This package is a full client-implementation of the client/server protocol for 'BaseX' and provides functionalities to create, manipulate and query on XML-data.

A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.

A novel bias-bound approach for non-parametric inference is introduced, 
    focusing on both density and conditional expectation estimation. 
    It constructs valid confidence intervals that account for the presence of 
    a non-negligible bias and thus make it possible to perform inference with 
    optimal mean squared error minimizing bandwidths. 
    This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.