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multinomialLogitMix  

Clustering Multinomial Count Data under the Presence of Covariates
View on CRAN: Click here


Download and install multinomialLogitMix package within the R console
Install from CRAN:
install.packages("multinomialLogitMix")

Install from Github:
library("remotes")
install_github("cran/multinomialLogitMix")

Install by package version:
library("remotes")
install_version("multinomialLogitMix", "1.1")



Attach the package and use:
library("multinomialLogitMix")
Maintained by
Panagiotis Papastamoulis
[Scholar Profile | Author Map]
First Published: 2022-08-23
Latest Update: 2023-07-17
Description:
Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) . 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) . 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) .
How to cite:
Panagiotis Papastamoulis (2022). multinomialLogitMix: Clustering Multinomial Count Data under the Presence of Covariates. R package version 1.1, https://cran.r-project.org/web/packages/multinomialLogitMix. Accessed 11 Apr. 2025.
Previous versions and publish date:
1.0 (2022-08-23 10:30)
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