Package: survClust
Title: Identification Of Clinically Relevant Genomic Subtypes Using
        Outcome Weighted Learning
Version: 1.3.0
Date: 2024-04-16
Authors@R: 
    person("Arshi", "Arora", , "arshiaurora@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-4040-1787"))
Description: survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types). 
VignetteBuilder: knitr
License: MIT + file LICENSE
Imports: Rcpp, MultiAssayExperiment, pdist, survival
LinkingTo: Rcpp
URL: https://github.com/arorarshi/survClust
BugReports: https://support.bioconductor.org/t/survClust
biocViews: Software, Clustering, Survival, Classification
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
LazyData: true
Depends: R (>= 3.5.0)
Suggests: knitr, testthat (>= 3.0.0), gplots, htmltools, BiocParallel
Config/testthat/edition: 3
git_url: https://git.bioconductor.org/packages/survClust
git_branch: devel
git_last_commit: 4135007
git_last_commit_date: 2025-04-15
Repository: Bioconductor 3.22
Date/Publication: 2025-06-04
NeedsCompilation: yes
Packaged: 2025-06-05 02:30:38 UTC; biocbuild
Author: Arshi Arora [aut, cre] (ORCID: <https://orcid.org/0000-0002-4040-1787>)
Maintainer: Arshi Arora <arshiaurora@gmail.com>
Built: R 4.5.0; x86_64-w64-mingw32; 2025-06-05 14:09:44 UTC; windows
Archs: x64
