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highMLR  

Feature Selection for High Dimensional Survival Data
View on CRAN: Click here


Download and install highMLR package within the R console
Install from CRAN:
install.packages("highMLR")

Install from Github:
library("remotes")
install_github("cran/highMLR")

Install by package version:
library("remotes")
install_version("highMLR", "0.1.1")



Attach the package and use:
library("highMLR")
Maintained by
Atanu Bhattacharjee
[Scholar Profile | Author Map]
First Published: 2021-05-11
Latest Update: 2022-07-18
Description:
Perform high dimensional Feature Selection in the presence of survival outcome. Based on Feature Selection method and different survival analysis, it will obtain the best markers with optimal threshold levels according to their effect on disease progression and produce the most consistent level according to those threshold values. The functions' methodology is based on by Sonabend et al (2021) and Bhattacharjee et al (2021) .
How to cite:
Atanu Bhattacharjee (2021). highMLR: Feature Selection for High Dimensional Survival Data. R package version 0.1.1, https://cran.r-project.org/web/packages/highMLR. Accessed 06 Apr. 2025.
Previous versions and publish date:
0.1.0 (2021-05-11 11:40)
Other packages that cited highMLR R package
View highMLR citation profile
Other R packages that highMLR depends, imports, suggests or enhances
Complete documentation for highMLR
Functions, R codes and Examples using the highMLR R package
Some associated functions: hnscc . mlclassCox . mlclassKap . mlhighCox . mlhighFrail . mlhighHet . mlhighKap . srdata . 
Some associated R codes: hnscc.R . mlclassCox.R . mlclassKap.R . mlhighCox.R . mlhighFrail.R . mlhighHet.R . mlhighKap.R . srdata.R .  Full highMLR package functions and examples
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