Package: compound.Cox
Type: Package
Title: Univariate Feature Selection and Compound Covariate for
        Predicting Survival, Including Copula-Based Analyses for
        Dependent Censoring
Version: 3.32
Date: 2025-1-11
Authors@R: c(person(given = "Takeshi",
                        family = "Emura",
                        role = c("aut", "cre"),
                        email = "takeshiemura@gmail.com"),
                 person(given = "Hsuan-Yu",
                        family = "Chen",
                        role = "aut"),
                 person(given = "Shigeyuki",
                        family = "Matsui",
                        role = "aut"),
                 person(given = "Yi-Hau",
                        family = "Chen",
                        role = "aut"))
Maintainer: Takeshi Emura <takeshiemura@gmail.com>
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) <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 Biomedicines) <DOI:10.3390/biomedicines11030797> and
 factorial survival analyses (Emura et al 2024 Stat Methods Med Res) <DOI:10.1177/09622802231215805>.
License: GPL-2
Depends: numDeriv, survival, MASS
Encoding: UTF-8
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2025-01-11 04:25:45 UTC; takes
Author: Takeshi Emura [aut, cre],
  Hsuan-Yu Chen [aut],
  Shigeyuki Matsui [aut],
  Yi-Hau Chen [aut]
Repository: CRAN
Date/Publication: 2025-01-11 06:00:06 UTC
