Description: Implements a pathwise algorithm for covariance selection

Title: Bias-Corrected Sandwich Variance Estimators for Marginal Cox
Authors@R: c(person("Xueqi","Wang", role = c("aut", "cre"),
person("Elizabeth", "Turner", role = "aut",
Description: The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).
Packaged: 2022-03-05 21:37:54 UTC; xueqiwang

Title: Cox survival models by likelihood based boosting
Description: This package provides routines for fitting Cox survival models by likelihood based boosting
Packaged: Fri Jan 11 18:35:02 2008; hornik

Title: Cox models by likelihood based boosting for a single survival endpoint or competing risks
Description: This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks
Packaged: Fri Feb 13 23:35:39 2009; harald

Title: Cox models by likelihood based boosting for a single survival
Description: This package provides routines for fitting Cox models by
likelihood based boosting for a single endpoint or in presence

Packaged: Sat Feb 6 11:58:12 2010; harald

Depends: R (>= 2.14.0), survival, Matrix, prodlim

Title: Variable Selection for Cox's Model with Interval-Censored Data
c(person("Qiwei", "Wu", role = c("aut", "cre"), email = "qw235@mail.missouri.edu"),
person("Jianguo", "Sun", role = c("aut")))
Description: Perform variable selection for Cox regression model with interval-censored data by using the methods proposed in Zhao et al. (2020) and Wu et al. (2020). Can deal with both low-dimensional and high-dimensional data.

Description: Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below.