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compound.Cox  

Univariate Feature Selection and Compound Covariate for Predicting Survival
View on CRAN: Click here


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

Install from Github:
library("remotes")
install_github("cran/compound.Cox")

Install by package version:
library("remotes")
install_version("compound.Cox", "3.30")



Attach the package and use:
library("compound.Cox")
Maintained by
Takeshi Emura
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-04-16
Latest Update: 2023-07-10
Description:
Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) , statistical methods in Emura et al (2012 PLoS ONE) , Emura & Chen (2016 Stat Methods Med Res) , and Emura et al (2019). Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023) .
How to cite:
Takeshi Emura (2012). compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival. R package version 3.30, https://cran.r-project.org/web/packages/compound.Cox
Previous versions and publish date:
1.0 (2012-04-16 10:35), 1.1 (2012-10-02 12:05), 1.2 (2012-11-10 10:40), 1.3 (2013-11-08 07:08), 1.4 (2014-04-13 19:51), 2.0 (2015-10-31 16:12), 2.1 (2016-10-21 13:27), 3.0 (2016-11-28 13:04), 3.1 (2017-01-09 15:16), 3.2 (2017-02-17 14:38), 3.3 (2017-03-18 18:52), 3.4 (2017-10-24 09:06), 3.5 (2017-11-02 10:01), 3.6 (2017-11-18 11:40), 3.7 (2018-01-03 07:30), 3.8 (2018-03-09 15:45), 3.9 (2018-03-16 10:15), 3.10 (2018-03-26 11:40), 3.11 (2018-05-24 14:07), 3.12 (2018-07-02 07:40), 3.13 (2018-07-12 06:50), 3.14 (2018-07-21 15:30), 3.15 (2019-04-03 10:40), 3.16 (2019-05-31 18:00), 3.17 (2019-06-02 05:40), 3.18 (2019-07-04 07:11), 3.19 (2019-09-19 14:40), 3.20 (2020-07-05 01:10), 3.21 (2022-08-08 15:40), 3.22 (2022-11-24 04:50), 3.23 (2022-12-06 09:10), 3.24 (2023-01-05 22:50), 3.25 (2023-01-21 10:30), 3.26 (2023-01-30 10:40), 3.27 (2023-02-13 11:20), 3.28 (2023-04-05 13:43), 3.29 (2023-06-18 06:20)
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