Title: Facilitates Clustered Binary Data Generation, and Estimation of
Intracluster Correlation Coefficient (ICC) for Binary Data
Authors@R: c(person("Akhtar", "Hossain", role = c("aut", "cre"),
person("Hrishikesh", "Chakraborty", role = "aut",
Description: Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 14 different methods, and 4 different types of confidence intervals.

Description: Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.

Title: Metadata Processing for the German Modification of the ICD-10
person("Ewan", "Donnachie", , "ewan@donnachie.net", c("aut", "cre"), , comment = c(ORCID = "0000-0002-0668-0049"))
Description: Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information <https://www.dimdi.de>, and are redistributed in accordance with their license.
Imports: magrittr, dplyr, purrr, tidyr, stringi, rlang, tibble
Suggests: testthat, knitr, rmarkdown, rvest
Author: Ewan Donnachie [aut, cre] (<https://orcid.org/0000-0002-0668-0049>)

Imports: magrittr, dplyr, purrr, tidyr, tidyselect, stringi, rlang,

Suggests: testthat (>= 3.0.0), knitr, rmarkdown, rvest

Title: Identification of Cancer Dysfunctional Subpathway by Integrating
DNA Methylation, Copy Number Variation, and Gene Expression
Author: Junwei Han,Baotong Zheng,Siyao Liu
Description: Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression.  2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.
Imports: igraph, graphite, metap, methods, org.Hs.eg.db