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baker: Bayesian Analysis Kit for Etiology Research

An R Package for Fitting Bayesian Nested Partially Latent Class Models

R build status AppVeyor build status

Maintainer: Zhenke Wu,

Issues: Please click here to report reproducible issues.

Vignette: Please click here to read the latest vignette.

Package website: Please click here for a website generated by pkgdown, which contains html format of the package manual.

References: If you are using baker for population and individual estimation from case-control data, please cite the following papers:

partially Latent Class Models (pLCM) Wu, Z., Deloria-Knoll, M., Hammitt, L. L., Zeger, S. L. and the Pneumonia Etiology Research for Child Health Core Team (2016), Partially latent class models for case–control studies of childhood pneumonia aetiology. J. R. Stat. Soc. C, 65: 97–114.
nested pLCM Wu, Z., Deloria-Knoll, M., Zeger, S.L.; Nested partially latent class models for dependent binary data; estimating disease etiology. Biostatistics 2017; 18 (2): 200-213.
nested pLCM regression Wu, Z., Chen, I (2021). Probabilistic Cause-of-disease Assignment using Case-control Diagnostic Tests: A Hierarchical Bayesian Approach. Statistics in Medicine 40(4):823-841.
Application Maria Deloria Knoll, Wei Fu, Qiyuan Shi, Christine Prosperi, Zhenke Wu, Laura L. Hammitt, Daniel R. Feikin, Henry C. Baggett, Stephen R.C. Howie, J. Anthony G. Scott, David R. Murdoch, Shabir A. Madhi, Donald M. Thea, W. Abdullah Brooks, Karen L. Kotloff, Mengying Li, Daniel E. Park, Wenyi Lin, Orin S. Levine, Katherine L. O’Brien, Scott L. Zeger; Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study, Clinical Infectious Diseases, Volume 64, Issue suppl_3, 15 June 2017, Pages S213–S227
Primary PERCH Analysis The PERCH Study Group (2019). Aetiology of severe hospitalized pneumonia in HIV-uninfected children from Africa and Asia: the Pneumonia Aetiology Research for Child Health (PERCH) Case-Control Study. The Lancet 394(10200): 757-779.
Software paper Chen I, Shi Q, Zeger SL, Wu Z (2022+) baker: An R package for Nested Partially-Latent Class Models.


# install.packages("devtools",repos="")


  • run install.packages("pbkrtest") for R(>=3.2.3) if this package is reported as missing.
  • Windows User: use devtools::install_github("zhenkewu/baker",INSTALL_opts=c("--no-multiarch")) instead if you see an error message ERROR: loading failed for 'i386' (Thanks Chrissy!).


devtools::install_github("zhenkewu/baker", build_vignettes=TRUE) # will take extra time to run a few examples.

Graphical User Interface (GUI)

# install.packages("devtools",repos="")
shiny::runApp(system.file("shiny", package = "baker"))

For developers interested in low-level details, here is a pretty awesome visualization of the function dependencies within the package:

library(DependenciesGraphs) # if not installed, try this-- devtools::install_github("datastorm-open/DependenciesGraphs")
library(QualtricsTools) # devtools::install_github("emmamorgan-tufts/QualtricsTools")
dep <- funDependencies('package:baker','nplcm')

You will get a dynamic figure. A snapshot is below:

Analytic Goal

  • To study disease etiology from case-control data from multiple sources that have measurement errors. If you are interested in estimating the population etiology pie (fraction), and the probability of each cause for individual case, try baker.

Comparison to Other Existing Solutions

  • Acknowledges various levels of measurement errors and combines multiple sources of data for optimal disease diagnosis.
  • Main function: nplcm() that fits the model with or without covariates.


  1. Implements hierarchical Bayesian models to infer disease etiology for multivariate binary data. The package builds in functionalities for data cleaning, exploratory data analyses, model specification, model estimation, visualization and model diagnostics and comparisons, catalyzing vital effective communications between analysts and practicing clinicians.
  2. baker has implemented models for dependent measurements given disease status, regression analyses of etiology, multiple imperfect measurements, different priors for true positive rates among cases with differential measurement characteristics, and multiple-pathogen etiology.
  3. Scientists in Pneumonia Etiology Research for Child Health (PERCH) study usually refer to the etiology distribution as “population etiology pie” and “individual etiology pie” for their compositional nature, hence the name of the package.


  • The baker package is compatible with OSX, Linux and Windows systems, each requiring a slightly different setup as described below. If you need to speed up the installation and analysis, please contact the maintainer or chat by clicking the gitter button at the top of this README file.

Connect R to JAGS/WinBUGS

Mac OSX 10.11 El Capitan

  1. Use Just Another Gibbs Sampler (JAGS)
  2. Install JAGS 4.2.0; Download here
  3. Install R; Download from here
  4. Fire up R, run R command install.packages("rjags")
  5. Run R command library(rjags) in R console; If the installations are successful, you’ll see some notes like this:
Loading required package: coda
Linked to JAGS 4.x.0
Loaded modules: basemod,bugs
  • Run R command library(baker). If the package ks cannot be loaded due to failure of loading package rgl, first install X11 by going here, followed by

Unix (Build from source without administrative privilege)

Here we use JHPCE as an example. The complete installation guide offers extra information.

  1. Download source code for JAGS 4.2.0;

  2. Suppose you’ve downloaded it in ~/local/jags/4.2.0. Follow the bash commands below:

    # change to the directory with the newly downloaded source files:
    cd ~/local/jags/4.2.0
    # create a new folder named "usr"
    mkdir usr
    # decompress files:
    tar zxvf JAGS-4.2.0.tar.gz
    # change to the directory with newly decompressed files:
    cd ~/local/jags/4.2.0/JAGS-4.2.0
    # specify new JAGS home:
    export JAGS_HOME=$HOME/local/jags/4.2.0/usr
    export PATH=$JAGS_HOME/bin:$PATH
    # link to BLAS and LAPACK:
    # Here I have used "/usr/lib64/atlas/" and "/usr/lib64/" on JHPCE that give me
    # access to and Please modify to paths on your system.
    LDFLAGS="-L/usr/lib64/atlas/ -L/usr/lib64/" ./configure --prefix=$JAGS_HOME --libdir=$JAGS_HOME/lib64 
    # if you have 8 cores:
    make -j8
    make install
    # prepare to install R package, rjags:
    export PKG_CONFIG_PATH=$HOME/local/jags/4.2.0/usr/lib64/pkgconfig 
    module load R
    R> install.packages("rjags")
    # or if the above fails, try:
    R>install.packages("rjags", configure.args="--enable-rpath")
  3. Also check out the INSTALLATION file for rjags package.

Submitting Jobs to Computing Cluster via a shell script

Again, I use JHPCE as an example.

#$ -M
#$ -N baker_regression_perch
#$ -o /users/zhwu/baker_regression/data_analysis/baker_regression_test.txt
#$ -e /users/zhwu/baker_regression/data_analysis/baker_regression_test.txt

export JAGS_HOME=$HOME/local/jags/4.2.0/usr
export PATH=$JAGS_HOME/bin:$PATH


cd /users/zhwu/baker_regression/data_analysis
#$ -cwd

echo "**** Job starts ****"
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: ${HOSTNAME}" 

Rscript real_regression_data_jhpce.R

echo "**** Job ends ****" 


  • JAGS 4.2.0
  1. Install R; Download from here
  2. Install JAGS 4.2.0; Add the path to JAGS 4.2.0 into the environmental variable (essential for R to find the jags program). See this for setting environmental variables;
  • alternatives are brew install -v jags for OSX, sudo apt-get install jags for Ubuntu/Debian
  1. Fire up R, run R command install.packages("rjags")
  2. Install Rtools (for building and installing R pacakges from source); Add the path to Rtools (e.g. C:\Rtools\) into your environmental variables so that R knows where to find it.

Example data sets

We provide two simulated data sets in the package: