Package: lcra 1.1.5

Michael Kleinsasser

lcra: Bayesian Joint Latent Class and Regression Models

For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology <doi:10.1097/EDE.0000000000001139>.

Authors:Michael Elliot [aut], Zhangchen Zhao [aut], Michael Kleinsasser [aut, cre]

lcra_1.1.5.tar.gz
lcra_1.1.5.tar.gz(r-4.7-any)lcra_1.1.5.tar.gz(r-4.6-any)
lcra_1.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
lcra/json (API)

# Install 'lcra' in R:
install.packages('lcra', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/umich-biostatistics/lcra/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

1.70 score 2 scripts 210 downloads 1 exports 4 dependencies

Last updated from:2ba5afa517. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK143
source / vignettesOK172
linux-release-x86_64OK113
wasm-releaseOK105

Exports:lcra

Dependencies:codalatticerjagsrlang