predieval
Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | predieval_0.1.1.tar.gz |
163.0 KiB |
0.1.1 |
rolling linux/noble R-4.5 | predieval_0.1.1.tar.gz |
163.0 KiB |
0.1.1 |
rolling source/ R- | predieval_0.1.1.tar.gz |
115.0 KiB |
0.1.1 |
latest linux/jammy R-4.5 | predieval_0.1.1.tar.gz |
163.0 KiB |
0.1.1 |
latest linux/noble R-4.5 | predieval_0.1.1.tar.gz |
163.0 KiB |
0.1.1 |
latest source/ R- | predieval_0.1.1.tar.gz |
115.0 KiB |
0.1.1 |
2026-04-26 source/ R- | predieval_0.1.1.tar.gz |
115.0 KiB |
0.1.1 |
2026-04-23 source/ R- | predieval_0.1.1.tar.gz |
115.0 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | predieval_0.1.1.zip |
166.2 KiB |
0.1.1 |
2025-04-20 source/ R- | predieval_0.1.1.tar.gz |
115.0 KiB |