# Bayesian Clustering Factor Models

## Documentation for package ‘BCFM’ version 1.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

BCFM.fit | Fit BCFM Model
BCFM.model.selection | BCFM Model Selection Over Multiple Groups and Factors
BCFMcpp | Gibbs sampler of BCFM
getmode | Get the mode of a vector
ggplot_B.CI | Build factor loadings plot
ggplot_B.trace | Trace plot for posterior of factor loadings
ggplot_IC | Plot IC Matrix from Model Selection
ggplot_latent.profiles | Plot Latent Factor Profiles by Cluster
ggplot_mu.density | Density of group means mu using ggplot2
ggplot_omega.density | The density plot of the diagonal of group covariance, Omega, with ggplot2
ggplot_probs.density | Density plot for posterior of probabilities
ggplot_probs.trace | Trace plot of probabilities parameter
ggplot_sigma2.CI | A credible interval plot of posterior of sigma squared
ggplot_tau.CI | A credible interval plot of posterior of factor loadings covariance, tau
ggplot_variability | Variability explained by factors
ggplot_Zit.heatmap | A heatmap of group assignments, Z using ggplot2
IC | Information Criterion. Very close to the original BIC method, but this uses the integrated likelihood instead.
init.data | Initialize Data Array for BCFM Model
initialize.cluster.hyperparms | Initialize cluster hyperparameters
initialize.hyp.parm | Initialize hyperparmeters for BCFM model
initialize.model.attributes | Build model attributes from the dataset
permutation.order | Order of permutation by the largest absolute value in each eigenvector
permutation.scale | Permute the dataset by the largest absolute value in each eigenvector, and scale
sim.data | Simulated data for BCFM model