compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival

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) <doi:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <doi:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <doi:10.1177/0962280214533378>, and Emura et al (2019)<doi:10.1016/j.cmpb.2018.10.020>. 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) <doi:10.3390/biomedicines11030797>.

Version:	3.30
Depends:	numDeriv, survival, MASS
Published:	2023-07-10
Author:	Takeshi Emura, Hsuan-Yu Chen, Shigeyuki Matsui, Yi-Hau Chen
Maintainer:	Takeshi Emura <takeshiemura at gmail.com>
License:	GPL-2
NeedsCompilation:	no
In views:	Survival
CRAN checks:	compound.Cox results

Documentation:

Reference manual:

compound.Cox.pdf

Downloads:

Package source:	compound.Cox_3.30.tar.gz
Windows binaries:	r-devel: compound.Cox_3.30.zip, r-release: compound.Cox_3.30.zip, r-oldrel: compound.Cox_3.30.zip
macOS binaries:	r-release (arm64): compound.Cox_3.30.tgz, r-oldrel (arm64): compound.Cox_3.30.tgz, r-release (x86_64): compound.Cox_3.30.tgz, r-oldrel (x86_64): compound.Cox_3.30.tgz
Old sources:	compound.Cox archive

Reverse dependencies:

Reverse depends:

Bivariate.Pareto, GFGM.copula, uni.survival.tree

Linking:

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