Provides a unified battery of goodness-of-fit and calibration tests
    for binary logistic regression, runnable in a single call via
    'run.all.gof()'. The package introduces the author's own tests aimed at
    sparse data --- the omnibus Ebrahim-Farrington (EF) test, the Directed EF
    ('EDGE') test that targets smooth calibration-shape departures, and a
    Cauchy-combination ensemble --- and aggregates a wide range of classical and
    modern tests for comparison, including Hosmer-Lemeshow, McCullagh,
    Osius-Rojek, le Cessie-van Houwelingen, Stute-Zhu, the binary-adaptive
    'BAGofT' test, and the 'GiViTI' calibration test (each obtained from its own
    package, where installed, and attributed to its authors). The tools are
    particularly suited to sparse data, where the Hosmer-Lemeshow test loses
    power. 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.

Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arXiv:1812.06948> for details.

Estimation tools for multidimensional Gaussian means using
    empirical Bayesian g-modeling. Methods are able to handle fully observed data as
    well as left-, right-, and interval-censored observations (Tobit
    likelihood); descriptions of these methods can be found in Barbehenn and
    Zhao (2023) <doi:10.48550/arXiv.2306.07239>. Additional, lower-level functionality based
    on Kiefer and Wolfowitz (1956) <doi:10.1214/aoms/1177728066> and Jiang and
    Zhang (2009) <doi:10.1214/08-AOS638> is provided that can be used to
    accelerate many empirical Bayes and nonparametric maximum likelihood
    problems.