This package is under development
Package website: release
mlr3cmprsk extends the mlr3 ecosystem with a unified interface for machine learning in competing risks survival analysis. It provides consistent task, learner, and prediction abstractions, enabling seamless benchmarking, model comparison, and integration with the broader mlr3 framework.
Installation
Install the development version from GitHub:
# install.packages("pak")
pak::pak("mlr-org/mlr3cmprsk")Example
library(mlr3cmprsk)
set.seed(42)
task = tsk("pbc")
task$select(c("age", "chol", "albumin", "bili"))
task$set_col_roles(cols = "status", add_to = "stratum")
learners = lrns(c("cmprsk.fg", "cmprsk.aalen"))
bm_grid = benchmark_grid(task, learners, rsmp("cv", folds = 3))
bm = benchmark(bm_grid)
# AUC at t = 100 (mean over causes, weighted by event frequencies)
bm$score(msr("cmprsk.auc", time = 100))[, .(task_id, learner_id, iteration, cmprsk.auc)]## task_id learner_id iteration cmprsk.auc
## 1: pbc cmprsk.fg 1 0.7813865
## 2: pbc cmprsk.fg 2 0.7663073
## 3: pbc cmprsk.fg 3 0.8390308
## 4: pbc cmprsk.aalen 1 0.5000000
## 5: pbc cmprsk.aalen 2 0.5000000
## 6: pbc cmprsk.aalen 3 0.5000000
# Brier score at t = 100 (mean over causes, weighted by event frequencies)
bm$score(msr("cmprsk.brier", time = 100))[, .(task_id, learner_id, iteration, cmprsk.brier)]## task_id learner_id iteration cmprsk.brier
## 1: pbc cmprsk.fg 1 0.1564736
## 2: pbc cmprsk.fg 2 0.1750273
## 3: pbc cmprsk.fg 3 0.1371346
## 4: pbc cmprsk.aalen 1 0.2182071
## 5: pbc cmprsk.aalen 2 0.2200391
## 6: pbc cmprsk.aalen 3 0.2181846For more competing risk learners, see the available list at mlr3extralearners.
For more details about available measures and their parameters, see reference list.
Code of Conduct
Please note that the mlr3cmprsk project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.