jmBIG
Joint Longitudinal and Survival Model for Big Data
Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.3 |
rolling linux/jammy R-4.5 | jmBIG_0.1.3.tar.gz |
252.0 KiB |
0.1.3 |
rolling linux/noble R-4.5 | jmBIG_0.1.3.tar.gz |
251.8 KiB |
0.1.3 |
rolling source/ R- | jmBIG_0.1.3.tar.gz |
137.4 KiB |
0.1.3 |
latest linux/jammy R-4.5 | jmBIG_0.1.3.tar.gz |
252.0 KiB |
0.1.3 |
latest linux/noble R-4.5 | jmBIG_0.1.3.tar.gz |
251.8 KiB |
0.1.3 |
latest source/ R- | jmBIG_0.1.3.tar.gz |
137.4 KiB |
0.1.3 |
2026-04-26 source/ R- | jmBIG_0.1.3.tar.gz |
137.4 KiB |
0.1.3 |
2026-04-23 source/ R- | jmBIG_0.1.3.tar.gz |
137.4 KiB |
0.1.3 |
2026-04-09 windows/windows R-4.5 | jmBIG_0.1.3.zip |
255.0 KiB |
0.1.3 |
2025-04-20 source/ R- | jmBIG_0.1.3.tar.gz |
137.4 KiB |