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

Package: unmconf 1.0.0

David Kahle

unmconf: Modeling with Unmeasured Confounding

Tools for fitting and assessing Bayesian multilevel regression models that account for unmeasured confounders.

Authors:Ryan Hebdon [aut], James Stamey [aut], David Kahle [aut, cre], Xiang Zhang [aut]

# Install 'unmconf' in R:

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

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

On CRAN:

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

Last updated from:6309f52a9b. Checks:4 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	140
source / vignettes	OK	214
linux-release-x86_64	OK	147
wasm-release	OK	110

Last update: 2024-09-10
Started: 2023-12-13

Help page	Topics
Convert to Greek expressions	drop_nulls expand_labels greek_expander helpers make_greek_coefs
Generate synthetic data	runm
Fitting Multilevel Bayesian Regression Model with Unmeasured Confounders	coef.unm_int jags_code print.unm_int unm_glm
Generate synthetic data	unm_backfill unm_dic unm_summary
unmconf: Modeling with Unmeasured Confounding	unmconf-package unmconf