Cox Mixed-Effects Model for Genome-Wide Association Studies
nloptr, MASS, matrixcalc, SNPRelate, rARPACK, stats, Matrix,
methods, utils, knitcitations, coxme

Sparse Estimation of Cox Proportional Hazards Models via
Approximated Information Criterion
Xiaogang Su and Razieh Nabi Abdolyousefi
Sparse estimation for Cox PH models is done via
Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe,
Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. 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.

Stochastic Gradient Descent log-Likelihood Estimation in Cox
Proportional Hazards Model
c(
person("Marcin",   "Kosinski", role = c("aut", "cre"), email = "m.p.kosinski@gmail.com"),
person("Przemyslaw", "Biecek", role = c("ctb"),        email = "przemyslaw.biecek@gmail.com")
)
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Marcin Kosinski [aut, cre],
Przemyslaw Biecek [ctb]

Cox regression with Firth's penalized likelihood
R by Meinhard Ploner, Fortran by Georg Heinze

Cox Regression with Firth's Penalized Likelihood
c(person("Georg", "Heinze", role=c("aut", "cre"), email="georg.heinze@meduniwien.ac.at"),
person("Meinhard", "Ploner", role=c("aut")))
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function).
The program fits profile penalized likelihood confidence intervals which were proved to outperform
Wald confidence intervals.
Georg Heinze [aut, cre],
Meinhard Ploner [aut]

c(person("Georg", "Heinze", role=c("aut", "cre"), email="georg.heinze@meduniwien.ac.at"),
person("Meinhard", "Ploner", role=c("aut")),
person("Lena", "Jiricka", role=c("aut")))
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see
Heinze and Schemper (2001) and Heinze and Dunkler (2008).
The program fits profile penalized likelihood confidence intervals which were proved to outperform
Wald confidence intervals.
Georg Heinze [aut, cre],
Meinhard Ploner [aut],
Lena Jiricka [aut]