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

R-Squared Measure Based on Partial LR Statistic, for the Cox PH
Regression Model
Calculate the R-squared, aka explained randomness, based on the partial likelihood ratio statistic under the Cox Proportional Hazard model [J O'Quigley, R Xu, J Stare (2005) <doi:10.1002/sim.1946>].

A package for fitting Cox models with penalized ridge-type
partial likelihood. The package includes functions for fitting
simple Cox models with all covariates controlled by a ridge
penalty. The weight of the penalty is optimised by using a REML
type-algorithm. Models with time varying effects of the
covariates can also be fitted. Some of the covariates may be
allowed to be fixed and thus not controlled by the penalty.
There are three different penalty functions, ridge, dynamic and
weighted dynamic. Time varying effects can be fitted without
the need of an expanded dataset.

A package for fitting Cox models with penalized ridge-type partial likelihood. The package includes functions for fitting simple Cox models with all covariates controlled by a ridge penalty. The weight of the penalty is optimised by using a REML type-algorithm. Models with time varying effects of the covariates can also be fitted. Some of the covariates may be allowed to be fixed and thus not controlled by the penalty. There are three different penalty functions, ridge, dynamic and weighted dynamic. Time varying effects can be fitted without the need of an expanded dataset.

Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty. In addition, it can truncate the estimate by a hard threshold, which is selected simultaneously with other tuning parameters. Moreover, it can handle the adaptive version of these regularization forms, such as adaptive lasso and net adjusting for signs of linked coefficients. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.