Authors@R: c(person('Juhee', 'Lee', role = c('aut', 'cre'),
person('YeongWoo', 'Park', role = 'aut',

Title: Latent (Variable) Analysis with Bayesian Learning
Description: An analytical framework for latent variables with different Bayesian learning methods, currently based on the partially confirmatory factor analysis (PCFA) model by Chen, Guo, Zhang, & Pan (2020) <DOI:10.1037/met0000293>.

Description: An analytical framework for latent variables with different Bayesian learning methods, including the partially confirmatory factor analysis (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>, its generalized version, and the partially confirmatory item response model (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>.

Description: A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Author: Haoxue Wang [aut, cre] (Development of the whole packages),
Li Xing [aut] (Authors of some functions),
Xuekui Zhang [aut] (Revison of this package),
Igor Burstyn [aut] (Revison of this package),
Paul Gustafson [aut] (Revison of this package)
Authors@R: c(person("Haoxue", "Wang", role = c("aut","cre"),
comment="Development of the whole packages",
person("Xuekui", "Zhang", role = c("aut"),
person("Igor", "Burstyn", role = c("aut"),
Description: The logistic Box-Cox model is a formal method, which can accommodate the non-linear
relationship between the log-odds and exposure via a shape parameter. Thus, it is
superior to the common two-step approach, which adds a data transformation step
before fitting a logistic regression model to accommodate any non-linearity in the
disease-exposure relationship. This package includes key functions in our model and
dataset for simulation. Simulation experiments can be reproduced following the Vignette.