frailtyMMpen
1.2.1Efficient Algorithm for High-Dimensional Frailty Model
Overview
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.
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- NOTE2026-06-090 OK · 13 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- WARNING2026-06-080 OK · 12 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-220 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-180 OK · 13 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-100 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 78%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 13%
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Code & Tests
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Package metadata
- First published
- 2023-03-24
- Total releases
- 4 / 3 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 627 KB / 4 files
- Download size
- 1.1 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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