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longit (version 0.1.0)

High Dimensional Longitudinal Data Analysis Using MCMC

Description

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) .

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Version

Install

install.packages('longit')

Monthly Downloads

135

Version

0.1.0

License

GPL-3

Maintainer

Atanu Bhattacharjee

Last Published

April 15th, 2021

Functions in longit (0.1.0)

Bysmxmss

Bayesian mixed model with random intercepts and random slopes for high dimensional longitudinal data with batch size.
creg

Bayesian multivariate regression with unstructured covariance matrix for high dimensional longitudinal data.
BysmxHPD

Bayesian mixed effect model for high dimensional longitduinal data with highest posterior density interval (HPDI).
gh

gh
Bysmixed

Bayesian mixed effect model with MCMC
Bysmxms

Bayesian mixed model with random intercepts and random slopes for high dimensional longitudinal data
hdmnarjg

Missing not at random by MCMC
msrep

longitudinal data
mvncovar2

Bayesian multivariate normal regression with unstructured covariance matrix for high dimensional longitudinal data.
longitdata

Repeatedly measured protein expression data
mvncovar1

Bayesian multivariate regression with independent covariance matrix for high dimensional longitudinal data.
repdata

longitudinal data
hdmarjg

Missing at ranom by MCMC
BysmxDIC

Bayesian mixed effect model for high dimensional longitduinal data with deviance information criterion (DIC).