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

TL;DR

The baker R package provides flexible tools for estimating nested partially-latent class models in case-control studies, facilitating population and individual-level inferences with comprehensive diagnostics and visualization.

Contribution

This work introduces the baker R package, enabling efficient Bayesian estimation of nested partially-latent class models for multivariate binary data in case-control designs.

Findings

Successfully applied to simulated data demonstrating model estimation.

Effectively analyzed real case-control datasets.

Enhanced communication between practitioners and domain scientists.

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. 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. Package URL: https://github.com/zhenkewu/baker