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.

Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.

Provides maximum likelihood methods to estimate bivariate dependency (correlation) from marginal summary statistics in multi-study settings. 
    The package supports both binary and continuous variables assumed to follow a bivariate normal distribution,
    enabling privacy-preserving joint estimation when individual-level data are unavailable.
    The binary method is fully described in the manuscript by Shang, Tsao and Zhang (2025) 
    <doi:10.48550/arXiv.2505.03995>: "Estimating the Joint Distribution of Two Binary Variables from Their Marginal Summaries".

Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.

Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.