Description: Versatile tools and data for graph matching analysis with various forms of prior information
that supports working with 'igraph' objects, matrix objects, or lists of either.
Suggests: spelling, dplyr (>= 0.5.0), testthat (>= 2.0.0), knitr,

Title: Computing Individual Components of the Gini and the Theil
Description: Computes individual contributions to the overall Gini and
Theil's T and Theil's L measures and their decompositions by groups
such as race, gender, national origin, with the three functions of
iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2020)
Depends: R (>= 3.6.0), foreach(>= 1.4.8), parallel, doParallel(>=

iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019)

Authors@R: c(person("Francesca", "Petralia", role = c("aut", "cre"),
person("Won-min", "Song", role = "aut"),
person("Adele", "Cutler", role = "ctb"),
person("Matthew", "Wiener", role = "ctb"))
Description: Integrative framework for the simultaneous estimation of interactions from different class of data.
Packaged: 2017-01-27 15:02:48 UTC; francescapetralia

Title: Improved Laplace Approximation for Integrals of Unimodal
person("Erlis", "Ruli", email = "erlisr@yahoo.it", role = c("aut", "cre")),
person("Nicola", "Sartori", role = "aut"),
person("Laura", "Ventura", role = "aut"))
Description: Improved Laplace approximation for integrals of unimodal functions. The method requires user-supplied R functions for: the integrand, its gradient and its Hessian matrix. The computations are run in parallel.
Imports: doParallel, foreach, iterators, Rcpp, stats
Author: Erlis Ruli [aut, cre], Nicola Sartori [aut], Laura Ventura [aut]

Description: Improved Laplace approximation for integrals of unimodal functions.
The method requires user-supplied R functions for: the integrand, its gradient
and its Hessian matrix. The computations are run in parallel.
Packaged: 2016-08-19 10:47:12 UTC; erlisruli

Title: Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, and
Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers)
evaluate patient images (cases). What does this mean? ... Many imaging studies are designed
so that every reader reads every case in all modalities, a fully-crossed study. In this case, the
data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects.
An MRMC analysis accounts for the variability and correlations from the readers and cases when
estimating variances, confidence intervals, and p-values. The functions in this package can treat
arbitrary study designs and studies with missing data, not just fully-crossed study designs.
The initial package analyzes the reader-average area under the receiver operating characteristic (ROC)
curve with U-statistics according to Gallas, Bandos, Samuelson,
Additional functions analyze other endpoints with U-statistics (binary performance
and score differences) following the work by Gallas, Pennello, and Myers 2007
Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.