Efficient Algorithm for High-Dimensional Frailty Model
cluster function
retrieve the coefficients under given tuning parameter
event function
Fitting frailty models with clustered, multi-event and recurrent data ...
Fitting penalized frailty models with clustered, multi-event and recur...
Plot the baseline hazard or the predicted hazard based on the new data
Plot the regularization path
Estimate the baseline hazard or the predict hazard rate based on the n...
Estimate the baseline hazard or the predict hazard rate based on the n...
print a non-penalized regression object
print a penalized regression object
Provide the summary for the model fitting
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>.