This package is considered a duplicate. The official version of this package is found at:https://atanubhattacharjee.r-universe.dev/longit

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.zip(r-4.7-any)longit_0.1.0.zip(r-4.6-any)
longit_0.1.0.tar.gz(r-4.7-any)longit_0.1.0.tar.gz(r-4.6-any)
longit_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
longit/json (API)

# Install 'longit' in R:

install.packages('longit', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

gh - Gh
longitdata - Repeatedly measured protein expression data
msrep - Longitudinal data
repdata - Longitudinal data

On CRAN:

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

jags cpp

1.00 score 275 downloads 10 exports 40 dependencies

Last updated from:2aa442df66. Checks:4 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel	NOTE	139
source / vignettes	OK	238
linux-release	NOTE	131
windows-devel	NOTE	100
windows-release	NOTE	98
wasm-release	OK	92

Exports:Bysmixed BysmxDIC BysmxHPD Bysmxms Bysmxmss creg hdmarjg hdmnarjg mvncovar1 mvncovar2

Dependencies:abind AICcmodavg boot cli coda codetools digest doRNG foreach glue iterators itertools lattice lifecycle magrittr MASS Matrix missForest nlme R2jags R2WinBUGS randomForest ranger rbibutils Rcpp RcppArmadillo RcppEigen Rdpack reformulas rjags rlang rngtools stringi stringr survival TMB unmarked vctrs VGAM xtable

Help page	Topics
Bayesian mixed effect model with MCMC	Bysmixed
Bayesian mixed effect model for high dimensional longitduinal data with deviance information criterion (DIC).	BysmxDIC
Bayesian mixed effect model for high dimensional longitduinal data with highest posterior density interval (HPDI).	BysmxHPD
Bayesian mixed model with random intercepts and random slopes for high dimensional longitudinal data	Bysmxms
Bayesian mixed model with random intercepts and random slopes for high dimensional longitudinal data with batch size.	Bysmxmss
Bayesian multivariate regression with unstructured covariance matrix for high dimensional longitudinal data.	creg
gh	gh
Missing at ranom by MCMC	hdmarjg
Missing not at random by MCMC	hdmnarjg
Repeatedly measured protein expression data	longitdata
longitudinal data	msrep
Bayesian multivariate regression with independent covariance matrix for high dimensional longitudinal data.	mvncovar1
Bayesian multivariate normal regression with unstructured covariance matrix for high dimensional longitudinal data.	mvncovar2
longitudinal data	repdata

Package: longit 0.1.0

longit: High Dimensional Longitudinal Data Analysis Using MCMC

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)