  In this paper, we describe an R package named coxphMIC, which implements the
sparse estimation method for Cox proportional hazards models via approximated
information criterion (Su et al., 2016 Biometrics). The developed methodology
is named MIC which stands for "Minimizing approximated Information Criteria". A
reparameterization step is introduced to enforce sparsity while at the same
time keeping the objective function smooth. As a result, MIC is computationally
fast with a superior performance in sparse estimation. Furthermore, the
reparameterization tactic yields an additional advantage in terms of
circumventing post-selection inference. The MIC method and its R implementation
are introduced and illustrated with the PBC data.
