Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) <doi:10.1093/biomet/78.4.719>.

Provides simple, fast, and stable functions to fit the normal
    means model using empirical Bayes. For available models and details, see 
    function ebnm(). Our JSS article, 
    Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>,
    provides a detailed introduction to the package.

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy.  No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for  each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

Implements the Ebrahim-Farrington goodness-of-fit test for logistic 
    regression models, particularly effective for sparse data and binary outcomes. 
    This test provides an improved alternative to the traditional Hosmer-Lemeshow 
    test by using a modified Pearson chi-square statistic with data-dependent 
    grouping. The test is based on Farrington (1996) theoretical framework but 
    simplified for practical implementation with binary data. Includes functions 
    for both the original Farrington test (for grouped data) and the new 
    Ebrahim-Farrington test (for binary data with automatic grouping). For more details see Hosmer (1980) <doi:10.1080/03610928008827941> and Farrington (1996) <doi:10.1111/j.2517-6161.1996.tb02086.x>.

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.