BCFM.fit                Fit BCFM Model
BCFM.model.selection    BCFM Model Selection Over Multiple Groups and
                        Factors
BCFMcpp                 Gibbs sampler of BCFM
IC                      Information Criterion. Very close to the
                        original BIC method, but this uses the
                        integrated likelihood instead.
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_Zit.heatmap      A heatmap of group assignments, Z using ggplot2
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
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
