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This package allows the fit and analysis of finite Mixtures of Mallows models with Spearman Distance for full and partial rankings with arbitrary missing positions. Inference is conducted within the maximum likelihood framework via Expectation-Maximization algorithms. Estimation uncertainty is tackled via diverse versions of bootstrapping as well as via Hessian-based standard errors calculations.
The most relevant reference of the methods is Crispino, Mollica, Astuti and Tardella (2023) https://link.springer.com/article/10.1007/s11222-023-10266-8
To install the current release, use
install.packages("MSmix")To install the current development version, use
#install.packages("remotes")
remotes::install_github("crispinomarta/MSmix")citation('MSmix')
#>To cite package 'MSmix' in publications use:
#>
#> Crispino, M., Mollica, C., Astuti, V., Tardella, L. (2023). Efficient and accurate inference for mixtures of
#> Mallows models with Spearman distance. Statistics and Computing, Vol. 33(98), pages 442--458, ISSN: 0960-3174,
#> DOI: 10.1007/s11222-023-10266-8.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {Efficient and accurate inference for mixtures of Mallows models
#> with Spearman distance},
#> author = {Marta Crispino and Cristina Mollica and Valerio Astuti and Luca Tardella},
#> year = {2023},
#> journal = {Statistics and Computing},
#> volume = {33},
#> number = {98},
#> issn = {0960-3174},
#> doi = {10.1007/s11222-023-10266-8},
#> }These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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