longit
High Dimensional Longitudinal Data Analysis Using MCMC
High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | longit_0.1.0.tar.gz |
69.9 KiB |
0.1.0 |
rolling linux/noble R-4.5 | longit_0.1.0.tar.gz |
69.5 KiB |
0.1.0 |
rolling source/ R- | longit_0.1.0.tar.gz |
13.3 KiB |
0.1.0 |
latest linux/jammy R-4.5 | longit_0.1.0.tar.gz |
69.9 KiB |
0.1.0 |
latest linux/noble R-4.5 | longit_0.1.0.tar.gz |
69.5 KiB |
0.1.0 |
latest source/ R- | longit_0.1.0.tar.gz |
13.3 KiB |
0.1.0 |
2026-04-26 source/ R- | longit_0.1.0.tar.gz |
13.3 KiB |
0.1.0 |
2026-04-23 source/ R- | longit_0.1.0.tar.gz |
13.3 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | longit_0.1.0.zip |
73.0 KiB |
0.1.0 |
2025-04-20 source/ R- | longit_0.1.0.tar.gz |
13.3 KiB |