hdCLdata

<p>This data include 50 clusters with 4 objects, a total of 200 events are recorded.
500 covariates can be used for model fitting. The data is generated from a
gamma frailty model with coefficients (5,5,5,5,...,0) and frailty variance 1.</p>

datasets

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Yunpeng Zhou

frailtyMMpen

Efficient Algorithm for High-Dimensional Frailty Model

Xifen Huang

Jinfeng Xu

hdCLdata function

<p>An object of class <code>data.frame</code> with 200 rows and 503 columns.</p>

hdCLdata: Simulated High-dimensional Clustered data

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