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  "Description": "The penalized and non-penalized Minorize-Maximization (MM)\nmethod for frailty models to fit the clustered data,\nmulti-event data and recurrent data. Least absolute shrinkage\nand selection operator (LASSO), minimax concave penalty (MCP)\nand smoothly clipped absolute deviation (SCAD) penalized\nfunctions are implemented. All the methods are computationally\nefficient. These general methods are proposed based on the\nfollowing papers, Huang, Xu and Zhou (2022)\n<doi:10.3390/math10040538>, Huang, Xu and Zhou (2023)\n<doi:10.1177/09622802221133554>.",
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      "title": "retrieve the coefficients under given tuning parameter",
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      "title": "#' cluster function #' #' @param x name from original dataframe which specifies the cluster or object id. #' @description Specify cluster id for clustered data or object id for recurrent data in the input 'formula'. #' @return No return value, called to construct 'formula'. #' @export cluster <- function(x) x event function",
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