CRAN/E | SurvivalClusteringTree

SurvivalClusteringTree

Clustering Analysis Using Survival Tree and Forest Algorithms

Installation

About

An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in .

Key Metrics

Version 1.0
Published 2023-09-11 81 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks SurvivalClusteringTree results

Downloads

Last 24 hours 0 -100%
Last 7 days 20 -44%
Last 30 days 142 -3%
Last 90 days 436
Last 365 days 436

Maintainer

Maintainer

Lu You

lu.you@epi.usf.edu

Authors

Lu You

aut / cre

(Created the package. Maintains the package.)

Lauric Ferrat

aut

(Added functionality. Revised the package. Wrote the vignette.)

Hemang Parikh

aut

(Checked and revised the package.)

Yanan Huo

aut

(Revised plotting functions of the package.)

Yuting Yang

aut

(Added some data frame features.)

Jeffrey Krischer

ctb

(Supervisor the medical research. Coauthor of the medical manuscript.)

Maria Redondo

ctb

(Principal investigators of the medical research. Coauthor of the medical manuscript.)

Richard Oram

ctb

(Coauthor of the medical manuscript.)

Andrea Steck

ctb

(Coauthor of the medical manuscript.)

Material

README
Reference manual
Package source

Vignettes

User Guide to SurvivalClusteringTree

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Imports

Rcpp
survival
dplyr
grid
gridtext
formula.tools

Suggests

knitr
rmarkdown
tinytest

LinkingTo

Rcpp
RcppArmadillo