The definitive reference for
baker R package can be found here.
This vignette describes and illustrates the functionality of the
R package. The package provides 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- and individual-level class distributions that may also depend on additional explanatory covariates.
baker implement recent methodological developments in our group (here, here, and here). 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 vital communications between practitioners and domain scientists. Package features and workflows are illustrated using simulated and real data sets.
The focus of this document is on guiding a new user to utilize some useful functions in
baker for simulation studies and data analyses, aided by other powerful
R packages. We refer readers of this document to the accompanying main software paper for more details about the software design considerations and review of model formulations. Since
baker’s first appearance on Github, the authors have not been able to track other recent substantive publications that have used this package; we hope the main software paper and this vignette serve as the definitive reference for future scientific studies that find the
baker package useful.