Title: Cox Models with Dynamic Ridge Penalties
Description: 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.

Author: Xiang Li, Donglin Zeng and Yuanjia Wang
Description: 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.

Description: Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty. In addition, it efficiently solves an approximate L0 variable selection based on truncated likelihood function. Moreover, it can also 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.

Description: Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter \code{lambda}. In addition, it fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.