MSmix1.0.2 package

Finite Mixtures of Mallows Models with Spearman Distance for Full and Partial Rankings

bicMSmix

BIC and AIC for mixtures of Mallows models with Spearman distance

bootMSmix

Print of the bootstrap confidence intervals for mixtures of Mallows mo...

bootstrapMSmix

Bootstrap confidence intervals for mixtures of Mallows models with Spe...

confintMSmix

Hessian-based confidence intervals for mixtures of Mallows models with...

data_augmentation

Data augmentation of partial rankings

data_censoring

Censoring of full rankings

data_completion

Completion of partial rankings with reference full rankings

data_conversion

Switch data format from rankings to orderings and vice versa

data_description

Descriptive summaries for partial rankings

expected_spear_dist

Expectation of the Spearman distance

fitMSmix

MLE of mixtures of Mallows models with Spearman distance via EM algori...

likMSmix

(Log-)likelihood for mixtures of Mallows models with Spearman distance

MSmix-package

Finite Mixtures of Mallows Models with Spearman Distance for Full and ...

partition_fun_spear

Partition function of the Mallows model with Spearman distance

plot.data_descr

Plot descriptive statistics for partial rankings

plot.dist

Plot the Spearman distance matrix

plot.emMSmix

Plot the MLEs for the fitted mixture of Mallows models with Spearman d...

rMSmix

Random samples from a mixture of Mallows models with Spearman distance

spear_dist

Spearman distance

spear_dist_distr

Spearman distance distribution under the uniform ranking model

summary.emMSmix

Summary of the fitted mixture of Mallows models with Spearman distance

var_spear_dist

Variance of the Spearman distance

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) <doi:10.1007/s11222-023-10266-8>.

  • Maintainer: Cristina Mollica
  • License: GPL (>= 3)
  • Last published: 2024-06-15