Package: longit 0.1.0
Atanu Bhattacharjee
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>.
Authors:
longit_0.1.0.tar.gz
longit_0.1.0.tar.gz(r-4.5-noble)longit_0.1.0.tar.gz(r-4.4-noble)
longit_0.1.0.tgz(r-4.4-emscripten)longit_0.1.0.tgz(r-4.3-emscripten)
longit.pdf |longit.html✨
longit/json (API)
| # Install 'longit' in R: |
| install.packages('longit', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- gh - Gh
- longitdata - Repeatedly measured protein expression data
- msrep - Longitudinal data
- repdata - Longitudinal data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:2aa442df66. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 08 2024 |
| R-4.5-linux | NOTE | Dec 08 2024 |
Exports:BysmixedBysmxDICBysmxHPDBysmxmsBysmxmsscreghdmarjghdmnarjgmvncovar1mvncovar2
Dependencies:abindAICcmodavgbootclicodacodetoolsdigestdoRNGforeachglueiteratorsitertoolslatticelifecyclelme4magrittrMASSMatrixminqamissForestnlmenloptrR2jagsR2WinBUGSrandomForestRcppRcppArmadilloRcppEigenrjagsrlangrngtoolsstringistringrsurvivalTMBunmarkedvctrsVGAMxtable
