Provides functions for evidence-based assessment of
    diagnostic tests. Includes calculation of common diagnostic
    accuracy statistics, Bayesian updating of probabilities,
    interpretation of likelihood ratios, and visualization using
    a Fagan nomogram.

Empirical Bayes thresholding using the methods developed
    by I. M. Johnstone and B. W. Silverman. The basic problem is to
    estimate a mean vector given a vector of observations of the mean
    vector plus white noise, taking advantage of possible sparsity in
    the mean vector. Within a Bayesian formulation, the elements of
    the mean vector are modelled as having, independently, a
    distribution that is a mixture of an atom of probability at zero
    and a suitable heavy-tailed distribution. The mixing parameter can
    be estimated by a marginal maximum likelihood approach. This leads
    to an adaptive thresholding approach on the original data.
    Extensions of the basic method, in particular to wavelet
    thresholding, are also implemented within the package.

We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2022) <doi:10.1080/01621459.2020.1777137>.

Computes empirical Bayes confidence estimators and confidence
    intervals in a normal means model. The intervals are robust in the sense
    that they achieve correct coverage regardless of the distribution of the
    means. If the means are treated as fixed, the intervals have an average
    coverage guarantee. The implementation is based on Armstrong, Kolesár and
    Plagborg-Møller (2020) <arXiv:2004.03448>.

Estimate prior variable weights for Bayesian Additive Regression
    Trees (BART). These weights correspond to the probabilities of the variables
    being selected in the splitting rules of the sum-of-trees.
    Weights are estimated using empirical Bayes and external information on 
    the explanatory variables (co-data).
    BART models are fitted using the 'dbarts' 'R' package.
    See Goedhart and others (2023) <doi:10.1002/sim.70004> for details.