Cox models by likelihood based boosting for a single survival endpoint or competing risks
This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks

Cox models by likelihood based boosting for a single survival
endpoint or competing risks
This package provides routines for fitting Cox models by
likelihood based boosting for a single endpoint or in presence
of competing risks

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

Variable Selection for Cox's Model with Interval-Censored Data
c(person("Qiwei", "Wu", role = c("aut", "cre"), email = "qw235@mail.missouri.edu"),
person("Hui", "Zhao", role = c("aut")),
person("Jianguo", "Sun", role = c("aut")))
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.
Qiwei Wu [aut, cre],
Hui Zhao [aut],
Jianguo Sun [aut]

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.

Lee, C.H., Liu, D.D., Ning, J., Zhou, H., and Shen, Y.
Performs analysis of right-censored length-biased data using Cox model. It contains model fitting and checking, and the stationarity assumption test. The model fitting and checking methods are described in Qin and Shen (2010) <doi:10.1111/j.1541-0420.2009.01287.x> and Lee, Ning, and Shen (2018) <doi:10.1007/s10985-018-9422-y>.

Cox Regression (Proportional Hazards Model) with Multiple Causes
and Mixed Effects
A high performance package estimating Proportional Hazards Model when an even can have more than one causes, including support for random and fixed effects, tied events, and time-varying variables.

A high performance package estimating Cox Model when an even have more than one causes, which also supports random and fixed effects, tied events, and time-varying variables.