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MixtureMissing (version 3.0.4)

Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random

Description

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random. Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, ) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, ). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic.

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Version

Install

install.packages('MixtureMissing')

Monthly Downloads

362

Version

3.0.4

License

GPL (>= 2)

Maintainer

Hung Tong

Last Published

February 4th, 2025

Functions in MixtureMissing (3.0.4)

print.MixtureMissing

Print for MixtureMissing
select_mixture

Mixture Model Selection
evaluation_metrics

Binary Classification Evaluation
hide_values

Missing Values Generation
UScost

US Cost of Living Indices in 2019 Data Set
MGHM

Multivariate Generalized Hyperbolic Mixture (MGHM)
generate_patterns

Missing-Data Pattern Generation
MCNM

Multivariate Contaminated Normal Mixture (MCNM)
extract

Extractor function for MixtureMissing
initialize_clusters

Cluster Initialization using a Heuristic Method
summary.MixtureMissing

Summary for MixtureMissing
auto

Automobile Data Set
bankruptcy

Bankruptcy Data Set
mean_impute

Mean Imputation
plot.MixtureMissing

MixtureMissing Plotting