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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>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | AICcmodavg, missForest, R2jags, rjags, utils |
| Published: | 2021-04-15 |
| DOI: | 10.32614/CRAN.package.longit |
| Author: | Atanu Bhattacharjee [aut, cre, ctb], Akash Pawar [aut, ctb], Bhrigu Kumar Rajbongshi [aut, ctb] |
| Maintainer: | Atanu Bhattacharjee <atanustat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | longit results |
| Reference manual: | longit.pdf |
| Package source: | longit_0.1.0.tar.gz |
| Windows binaries: | r-devel: longit_0.1.0.zip, r-release: longit_0.1.0.zip, r-oldrel: longit_0.1.0.zip |
| macOS binaries: | r-release (arm64): longit_0.1.0.tgz, r-oldrel (arm64): longit_0.1.0.tgz, r-release (x86_64): longit_0.1.0.tgz, r-oldrel (x86_64): longit_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.