baker 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 Cited in: 0 Documents Standard Articles 1 Publication describing the Software Year baker: An R package for Nested Partially-Latent Class Models Irena B Chen, Qiyuan Shi, Scott L Zeger, Zhenke Wu 2022