Authors@R: person("John", "Ferguson", email = "john.ferguson@nuigalway.ie", role = c("aut", "cre"))
Description: 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.

Title: Equal Covariance Functions Testing by L2-Norm and Sup-Norm
Author: Jia Guo, Bu Zhou, Jin-Ting Zhang
Description: Testing the equality of several covariance functions of functional data. Four different methods are implemented: L2-norm with W-S naive, L2-norm with W-S bias-reduced, L2-norm (Zhang 2013) <ISBN:9781439862735>, and sup-norm with resampling (Guo et al. 2017) <arXiv:1609.04232>.
License: GNU Lesser General Public License

Title: A Goodness-of-Fit Test for Elliptical Distributions with
Authors@R: c(person("Gilles R", "Ducharme", role = c("aut"),
person("Pierre", "Lafaye De Micheaux", role = c("aut", "cre"),
Depends: R (>= 3.3.0), orthopolynom, bootstrap
Description: A goodness-of-fit test for elliptical distributions with diagnostic
capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2019) <arXiv:1902.03622>.

Title: Ensemble Correlation-Based Low-Rank Matrix Completion
Description: Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.
Authors@R: c(person(given = "Mahdi", family = "Ghadamyari", role=c("aut", "cre"), email="ghadamy@uwindsor.ca"), person(given = "Mehdi", family = "Naseri", role=c("aut"), email="naseri@uwindsor.ca"))