Yuanhui Xiao <matyxx@langate.gsu.edu> and Ye Cui

The package contains two functions for L1-version of
Cramer-von Mises Tests: one computes Cramer-von Mises two
sample test scores, the other cmoputes the exact p-value(s) for
given Cramer-von Mises two-sample test score(s) under the
assumption that the populations under comparison have the same
probability distribution. The L1-version Cramer-von Mises test,
like its L2 counterpart, is distribution-free, but of less
computational intensity. In certain cases, this version of
Cramer-von Mises test is almost as powerful as its L2
counterpart. Simulation study also shows that it is more
powerful than the Kolmogorov-Smirnov test in certain cases.

The package contains two functions: one computes the Cramer-von Mises
two sample test scores, the other cmoputes the exact p-value(s) for given
Cramer-von Mises two-sample test score(s) under the assumption that
the populations under comparison have the same probability distribution.
Like Kolmogorov-Smirnov two-sample test, Cramer-von Mises test
is also distribution free. In certain cases, the Cramer-von Mises test
is more powerful than the Kolmogorov-Smirnov test, but it is less widely
used than the latter.

The package contains two functions: one computes the
Cramer-von Mises two sample test scores, the other cmoputes the
exact p-value(s) for given Cramer-von Mises two-sample test
score(s) under the assumption that the populations under
comparison have the same probability distribution. Like
Kolmogorov-Smirnov two-sample test, Cramer-von Mises test is
also distribution free. In certain cases, the Cramer-von Mises
test is more powerful than the Kolmogorov-Smirnov test, but it
is less widely used than the latter.

Cyclic Coordinate Descent for Logistic, Poisson and Survival
Analysis
Marc A. Suchard [aut, cre],
Martijn J. Schuemie [aut],
Trevor R. Shaddox [aut]
This model fitting tool incorporates cyclic coordinate descent and
majorization-minimization approaches to fit a variety of regression models
found in large-scale observational healthcare data.  Implementations focus
on computational optimization and fine-scale parallelization to yield
efficient inference in massive datasets.
Matrix, Rcpp (>= 0.12.4), bit, ff, ffbase, RcppParallel,
methods
Rcpp, BH (>= 1.51.0), RcppEigen (>= 0.3.2), RcppParallel