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Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

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3.9Kdownloads / year
–test coverage
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Overview

About

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) DOI:10.1007/s11222-019-09891-z (2018) DOI:10.1016/j.csda.2018.03.007). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) DOI:10.1111/j.1467-9868.2011.00781.x): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) DOI:10.1007/s11222-008-9056-0). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) DOI:10.1198/jcgs.2010.09008, Papastamoulis (2016) DOI:10.18637/jss.v069.c01).

Install

Health

CRAN checks
13OK
Slowest check: 4.3 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-04-22
  • ERROR13 OK, 1 ERROR2026-04-18
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 5.1
READMENoVignettesNoNEWSNoCitation fileYes · cites a publicationWebsitenot declared in DESCRIPTION
Description cites a JSS article
Help pages
68includes a package overview page
Examples that run
100%41 run in a plain check
Exported functions with examples
61%
Documented parameters
not tracked
Return-value docs
not tracked
Help pages citing references
7%

Downloads

3.9K
CRAN downloads in the past year
Rank #11,073 · ~11/day · ~323/mo
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37230 days
96190 days
3.9K1 year
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Repository

1Stars
0Forks
0Open issues
0Open PRs
0Releases
116Commits
2Commit identities
116 commits · Last activity 2020-02-20 · 0% stars, 30d
Responsiveness
not measured

Stars over time

2017-01-18 · 12026-07-07 · 1
PracticesBeta

Checks run against github.com/mqbssppe/overfittingfabmix on 2026-10-22.

No development-tooling practices detected in the upstream repository.

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
12 external dependencies (excludes base and recommended)

Installing fabMix adds 29 packages (12 with compiled code, 25 MB of source tarballs). Heaviest: ggplot2 6.0 MB, corrplot 3.6 MB, mclust 2.7 MB.

Depends (0)
none
LinkingTo (2)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
1direct
0indirect
Imports 1
Depends, Imports or LinkingTo, most downloaded first

Datasets

People & History

People (1)
Author, Maintainer since 4.3 (2018)
Releases (6)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 5.1Latest
    record
    2024-02-12 · current release · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • 5.0
    record
    2020-02-19 · diff ↗
  • 4.6
    record
    2020-01-21 · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • 4.5
    record
    2019-01-08 · diff ↗
  • 4.4
    record
    2018-11-28 · diff ↗
  • 4.3
    record
    2018-10-30
  • R
    R 3.5.0 released · 2018-04-23

Package metadata

First published
2018-10-30
Total releases
6 / 8 yrs
License
GPL-2 OSI
Bundled data
68 KB / 1 file
Download size
131 KB

Cite

Cite this package

Run in R for the authors' entry:

citation("fabMix")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1007/s11222-019-09891-z.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for fabMix version 5.1 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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