Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: Bayesian Exploratory Factor Analysis (befa), an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling. More approaches will be included in future releases of this package.
|Author||Rémi Piatek [aut, cre]|
|Date of publication||2017-02-20 11:19:55|
|Maintainer||Rémi Piatek <firstname.lastname@example.org>|
BayesFM: BayesFM: Package for Bayesian Factor Modeling
befa: Bayesian Exploratory Factor Analysis
plot.befa: Plot object of class 'befa'
post.column.switch: Perform column switchting on posterior MCMC sample
post.sign.switch: Perform sign switchting on posterior MCMC sample
simul.dedic.facmod: Generate synthetic data from a dedicated factor model
simul.nfac.prior: Simulate prior distribution of number of latent factors
simul.R.prior: Simulate prior distribution of factor correlation matrix
summary.befa: Summarize 'befa' object
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