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:Atanu Bhattacharjee [aut, cre, ctb], Akash Pawar [aut, ctb], Bhrigu Kumar Rajbongshi [aut, ctb]

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'))

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 171 downloads 10 exports 39 dependencies

Last updated 4 years agofrom:2aa442df66. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 09 2024
R-4.5-linuxNOTEOct 09 2024

Exports:BysmixedBysmxDICBysmxHPDBysmxmsBysmxmsscreghdmarjghdmnarjgmvncovar1mvncovar2

Dependencies:abindAICcmodavgbootclicodacodetoolsdigestdoRNGforeachglueiteratorsitertoolslatticelifecyclelme4magrittrMASSMatrixminqamissForestnlmenloptrR2jagsR2WinBUGSrandomForestRcppRcppArmadilloRcppEigenrjagsrlangrngtoolsstringistringrsurvivalTMBunmarkedvctrsVGAMxtable