## frailtyMMpen:
Package for Penalized Frailty Models

This package implements the MM algorithm for a variety types of
frailty models which can handle clustered data, multi-event data and
recurrent data in addition to the simple frailty model. Besides, this
package can obtain the estimation of parameters for penalized regression
using LASSO, MCP and SCAD penalties. Currently supported frailty
distributions include gamma, log-normal, inverse gaussian and PVF
(1<p<2). The estimation procedure is computationally efficient
which makes it also capable for handling high-dimensional data.

## Installation

You can install developed version of frailtyMMpen from github
with:

```
# install.packages("devtools")
devtools::install_github("heilokchow/frailtyMMpen")
```

## Example

This is a basic example which shows you how to use this package, you
may refer to the package manual for detailed descriptions and examples
for each function.

We use the simulated data with 50 clusters and 10 objects in each
cluster:

We first run the non-penalized regression with Gamma frailty and
obtain the summary statistics and the plot of conditional baseline
hazard.

```
gam_cl = frailtyMM(Surv(time, status) ~ . + cluster(id), simdataCL, frailty = "gamma")
summary(gam_cl)
plot(gam_cl)
```

Then, we perform the penalized regression with Gamma frailty and
LASSO penalty and obtain BIC, degree of freedom under a sequence of
tuning parameters and the plot of regularization path.

```
gam_cl_pen = frailtyMMpen(Surv(time, status) ~ . + cluster(id), simdataCL, frailty = "gamma")
print(gam_cl_pen)
plot(gam_cl_pen)
```