Depends: R (>= 3.0.1), lattice, coda, MASS, MCMCpack
Description: This package implements a model for joint meta-analysis of sensitivity and specificity of the diagnostic test under evaluation, while taking into account the possibly imperfect sensitivity and specificity of the reference test. This hierarchical model accounts for both within and between study variability. Estimation is carried out using a Bayesian approach, implemented via a Gibbs sampler. The model can be applied in situations where more than one reference test is used in the selected studies.
Packaged: 2013-08-22 15:00:39 UTC; ian.schiller

Title: Meta-Analysis of Diagnostic Test Accuracy when Reference Test is
Depends: R (>= 3.1.2), lattice, coda, MASS, MCMCpack
Description: Implements a model for joint meta-analysis of sensitivity and specificity of the diagnostic test under evaluation, while taking into account the possibly imperfect sensitivity and specificity of the reference test. This hierarchical model accounts for both within and between study variability. Estimation is carried out using a Bayesian approach, implemented via a Gibbs sampler. The model can be applied in situations where more than one reference test is used in the selected studies.
Packaged: 2015-02-09 19:01:28 UTC; ian.schiller

Depends: R (>= 3.5.0), lattice, coda, MASS, MCMCpack
Packaged: 2019-09-18 18:01:25 UTC; ischiller

Title: Biclustering with heterogeneous variance
Authors@R: c(person("Guanhua", "Chen", role = c("aut","cre"), email =
"guanhuac@live.unc.edu"), person("Michael", "Kosorok", role =
Description: HSSVD is a recently developed data mining tool for discovering subgroups of patients and genes which simultaneously display unusual levels of variability compared to other genes and patients. Previous biclustering methods were restricted to mean level detection, while the new method can detect both mean and variance biclusters.

Title: Biclustering with Heterogeneous Variance
Description: A data mining tool for discovering subgroups of patients and genes that simultaneously display unusual levels of variability compared to other genes and patients. Based on sparse singular value decomposition (SSVD), the method can detect both mean and variance biclusters in the presence of heterogeneous residual variance.