Penalized Generalized Estimating Equations for Bivariate Mixed
Outcomes

Perform simultaneous estimation and variable selection for correlated bivariate
mixed outcomes (one continuous outcome and one binary outcome per cluster) using
penalized generalized estimating equations. In addition, clustered Gaussian and binary
outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
Cross-validation can be performed to find the optimal regularization parameter(s).

Penalized Generalized Estimating Equations for Bivariate Mixed Outcomes

Perform simultaneous estimation and variable selection for correlated
bivariate mixed outcomes (one continuous outcome and one binary outcome
per cluster) using penalized generalized estimating equations. In
addition, clustered Gaussian and binary outcomes can also be modeled.
The SCAD, MCP, and LASSO penalties are supported. Cross-validation can
be performed to find the optimal regularization parameter(s).

Installation from GitHub:

if (!require("devtools"))

install.packages("devtools")

devtools::install_github("kaos42/pgee.mixed")

Installation note for OS X users:

This package uses Rcpp and RcppArmadillo. Mac OS X users may see the following
error in their console while trying to install the package:

ld: warning: directory not found for option '-L/usr/local/lib/gcc/x86_64-apple-darwin13.0.0/4.8.2'
ld: library not found for -lgfortran

The solution is documented here. In a nutshell, type the following in a terminal:

curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

To do list

If there is sufficient interest in this package, the following features could
be added:

Families other than Gaussian and binomial.

Working correlation structures other than independence, compound symmetry, and AR(1).

Specify a vector of cluster ids rather than force the equal cluster size structure.

Users can provide fixed working correlation and dispersion parameters.

Users can provide an index vector specifying which parameters are not to be penalized.