swMATH ID: 41366
Software Authors: Irena B Chen, Qiyuan Shi, Scott L Zeger, Zhenke Wu
Description: baker: An R package for Nested Partially-Latent Class Models. 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
Homepage: https://arxiv.org/abs/2202.11778
Source Code:  https://github.com/zhenkewu/baker
Dependencies: R
Keywords: arXiv_stat.ME; arXiv_stat.AP; arXiv_stat.CO; R; R package; NPLCM; Nested Partially-Latent Class Models; JAGS; Case-control studies; Latent class models; Measurement error; Markov chain Monte Carlo
Related Software: BayesLCA; poLCA; JAGS; R; CRAN Task Views; ggmcmc; CODA; randomLCA
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baker: An R package for Nested Partially-Latent Class Models
Irena B Chen, Qiyuan Shi, Scott L Zeger, Zhenke Wu