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coxphMIC  

Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criterion
View on CRAN: Click here


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

Install from Github:
library("remotes")
install_github("cran/coxphMIC")

Install by package version:
library("remotes")
install_version("coxphMIC", "0.1.0")



Attach the package and use:
library("coxphMIC")
Maintained by
Xiaogang Su
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-04-26
Latest Update:
Description:
Sparse estimation for Cox PH models is done via Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, Fan, and Zhang (2016) . MIC mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. The problem is further reformulated with a re-parameterization step so that it reduces to one unconstrained non-convex yet smooth programming problem, which can be solved efficiently. Furthermore, the re-parameterization tactic yields an additional advantage in terms of circumventing post-selection inference.
How to cite:
Xiaogang Su (2017). coxphMIC: Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criterion. R package version 0.1.0, https://cran.r-project.org/web/packages/coxphMIC. Accessed 07 Mar. 2026.
Previous versions and publish date:
0.1.0 (2017-04-26 07:56)
Other packages that cited coxphMIC R package
View coxphMIC citation profile
Other R packages that coxphMIC depends, imports, suggests or enhances
Functions, R codes and Examples using the coxphMIC R package
Some associated functions: LoglikPen . coxphMIC . plot.coxphMIC . print.coxphMIC . 
Some associated R codes: PenLoglik.R . coxphMIC.R . plot-coxphMIC.R . print-coxphMIC.R .  Full coxphMIC package functions and examples
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