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MixtureMissing  

Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("MixtureMissing", "3.0.6")



Attach the package and use:
library("MixtureMissing")
Maintained by
Hung Tong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-10-20
Latest Update: 2025-02-04
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.
How to cite:
Hung Tong (2021). MixtureMissing: Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random. R package version 3.0.6, https://cran.r-project.org/web/packages/MixtureMissing. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0.0 (2021-10-20 08:40), 1.0.1 (2022-01-28 21:40), 1.0.2 (2022-01-31 00:00), 2.0.0 (2023-04-14 00:40), 3.0.0 (2023-12-15 22:50), 3.0.1 (2024-01-17 18:52), 3.0.2 (2024-03-19 23:10), 3.0.3 (2024-10-15 23:50), 3.0.4 (2025-02-04 23:10), 3.0.5 (2025-10-23 17:50)
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