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.zip(r-4.7-any)lcra_1.1.5.zip(r-4.6-any)lcra_1.1.5.zip(r-4.5-any)
lcra_1.1.5.tgz(r-4.6-any)lcra_1.1.5.tgz(r-4.5-any)
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✨
DESCRIPTION
card.svg |card.png
lcra/json (API)

# Install 'lcra' in R:
install.packages('lcra', repos = c('https://umich-biostatistics.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
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

2.70 score 2 scripts 287 downloads 1 exports 4 dependencies

Last updated from:6bd299a67e. Checks:2 NOTE, 7 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE96
source / vignettesOK149
linux-releaseOK94
macos-releaseOK81
macos-oldrelOK101
windows-develNOTE59
windows-releaseOK50
windows-oldrelOK53
wasm-releaseOK84

Exports:lcra

Dependencies:codalatticerjagsrlang