baker: An R package for Nested Partially-Latent Class Models

Irena Chen, Qiyuan Shi, Scott Zeger, Zhenke Wu (2022+). Submitted.
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Abstract

This paper describes and illustrates the functionality of the baker R package. The package estimates a suite of nested partially-latent class models (NPLCM) for multivariate binary responses that are observed under a case-control design. The baker package allows researchers to flexibly estimate population-level class prevalences and posterior probabilities of class membership for individual cases that may also depend on explanatory covariates. Estimation is accomplished by calling a cross-platform automatic Bayesian inference software JAGS through a wrapper R function that parses model specifications and data inputs. The baker package provides many useful features, including data ingestion, exploratory data analyses, model diagnostics, extensive plotting and visualization options, catalyzing communications between practitioners and domain scientists. Package features and workflows are illustrated using simulated and real data sets.

Keywords Case-control studies, Latent class models, Measurement error, Markov chain Monte Carlo, R, JAGS