This package is considered a duplicate. The official version of this package is found at:https://ategge.r-universe.dev/BCFM

Package: BCFM 1.0.0

Meriem Touami

BCFM: Bayesian Clustering Factor Models

Implements the Bayesian Clustering Factor Models (BCFM) for simultaneous clustering and latent factor analysis of multivariate longitudinal data. The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via 'Rcpp'. Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) <doi:10.1002/sim.70350>.

Authors:Allison Tegge [aut], Marco Ferreira [aut], Hwasoo Shin [aut], Meriem Touami [aut, cre]

BCFM_1.0.0.tar.gz
BCFM_1.0.0.tar.gz(r-4.7-arm64)BCFM_1.0.0.tar.gz(r-4.7-x86_64)BCFM_1.0.0.tar.gz(r-4.6-arm64)BCFM_1.0.0.tar.gz(r-4.6-x86_64)
BCFM_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BCFM/json (API)

# Install 'BCFM' in R:

install.packages('BCFM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ategge/bcfm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

sim.data - Simulated data for BCFM model

On CRAN:

openblas cpp

2.70 score 3 scripts 248 downloads 23 exports 83 dependencies

Last updated from:1db222df3a. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	204
linux-devel-x86_64	OK	206
source / vignettes	OK	314
linux-release-arm64	OK	382
linux-release-x86_64	OK	231
wasm-release	OK	183

Exports:BCFM.fit BCFM.model.selection BCFMcpp getmode ggplot_B.CI ggplot_B.trace ggplot_IC ggplot_latent.profiles ggplot_mu.density ggplot_omega.density ggplot_probs.density ggplot_probs.trace ggplot_sigma2.CI ggplot_tau.CI ggplot_variability ggplot_Zit.heatmap IC init.data initialize.cluster.hyperparms initialize.hyp.parm initialize.model.attributes permutation.order permutation.scale

Dependencies:abind backports boot broom car carData cli colorspace corrplot cowplot cpp11 Deriv doBy dplyr farver fastmatrix forecast Formula fracdiff generics ggplot2 ggpubr ggrepel ggsci ggsignif glue GPArotation gridExtra gtable isoband labeling LaplacesDemon lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa mnormt modelr mvtnorm nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig polynom psych purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rstatix S7 scales SparseM stringi stringr survival tibble tidyr tidyselect timeDate urca utf8 vctrs viridisLite withr zoo

Getting Started with BCFM: A Complete Workflow

Allison Tegge, Virginia Tech

Rendered fromintroduction-to-BCFM.Rmdusingknitr::rmarkdownon Jun 10 2026.

Last update: 2026-02-10
Started: 2026-02-10

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

Package: BCFM 1.0.0

BCFM: Bayesian Clustering Factor Models

Getting Started with BCFM: A Complete Workflow

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)