Package: longit 0.1.0

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]

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Last updated 3 years agofrom:2aa442df66. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:BysmixedBysmxDICBysmxHPDBysmxmsBysmxmsscreghdmarjghdmnarjgmvncovar1mvncovar2

Dependencies:abindAICcmodavgbootclicodacodetoolsdigestdoRNGforeachglueiteratorsitertoolslatticelifecyclelme4magrittrMASSMatrixminqamissForestnlmenloptrR2jagsR2WinBUGSrandomForestRcppRcppArmadilloRcppEigenrjagsrlangrngtoolsstringistringrsurvivalTMBunmarkedvctrsVGAMxtable