c(person('Juhee', 'Lee', role = c('aut', 'cre'),
email = 'ljh988488@gmail.com'),
person('Young-Min', 'Kim', role = 'aut',
email = 'kymmyself@knu.ac.kr'),
person('YeongWoo', 'Park', role = 'aut',
email = 'yeong1991@naver.com'),
person('Eunjin', 'Jang', role = 'aut',
email = 'yeong1991@naver.com'),
person('Jinkyung', 'Yoo', role = 'aut',
email = 'w.yoojinky@gmail.com'),
person('Songwon', 'Seo', role = 'ctb',
email = 'songwon@gmail.com'),
person('Eun-Shil', 'Cha', role = 'ctb',
email = 'eunshiri83@korea.ac.kr'),
person('Won-Jin', 'Lee', role = 'ctb',
email = 'leewj@korea.ac.kr'))
Juhee Lee [aut, cre],
Young-Min Kim [aut],
YeongWoo Park [aut],
Eunjin Jang [aut],
Jinkyung Yoo [aut],
Songwon Seo [ctb],
Eun-Shil Cha [ctb],
Won-Jin Lee [ctb]

Latent (Variable) Analysis with Bayesian Learning
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>.

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>.

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.

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)
c(person("Haoxue", "Wang", role = c("aut","cre"),
comment="Development of the whole packages",
email="haoxwang@student.ethz.ch"),
person("Li", "Xing", role = c("aut"),
comment="Authors of some functions"),
person("Xuekui", "Zhang", role = c("aut"),
comment="Revison of this package"),
person("Igor", "Burstyn", role = c("aut"),
comment="Revison of this package"),
person("Paul", "Gustafson",role=c("aut"),
comment="Revison of this package"))
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.