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multinomialLogitMix

1.1

Clustering Multinomial Count Data under the Presence of Covariates

0packages depend
2.6Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Panagiotis PapastamoulisFirst published 2022-08-232 releasesCRAN page ↗

Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) DOI:10.1007/s11634-023-00547-5. These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) DOI:10.1111/j.1467-9868.2011.00781.x. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) DOI:10.18637/jss.v069.c01.

Install

Health

CRAN checks
13OK
Slowest check: 3.5 min · r-devel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
10
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-04-22
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
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Examples that run
100%
Documented parameters
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Return-value docs
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References docs
44%

Downloads

2.6K
CRAN downloads in the past year
Rank #15,037 · ~7/day · ~214/mo
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14130 days
69290 days
2.6K1 year
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Dependencies

Declared dependencies
11 external dependencies (excludes base and recommended)
Depends (0)
none
LinkingTo (2)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

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Code & Tests

People & History

People (1)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

  • 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
  • 1.1Latest
    2023-07-17 · current release · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • 1.0
    2022-08-23
  • R
    R 4.2.0 released · 2022-04-22

Package metadata

First published
2022-08-23
Total releases
2 / 4 yrs
License
GPL-2 OSI
Download size
31 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("multinomialLogitMix")
Papastamoulis, P. (2023). multinomialLogitMix: Clustering Multinomial Count Data under the Presence of Covariates (Version 1.1) [Computer software]. https://doi.org/10.32614/CRAN.package.multinomialLogitMix

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

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 multinomialLogitMix version 1.1 [Data set]. HJJB, LLC. Data release v2026-08-17. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-17, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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