Crandore Hub

MixtureMissing

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

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, <doi:10.18637/jss.v115.i03>). Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). 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. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.

Versions across snapshots

VersionRepositoryFileSize
3.0.6 rolling linux/jammy R-4.5 MixtureMissing_3.0.6.tar.gz 275.1 KiB
3.0.6 rolling linux/noble R-4.5 MixtureMissing_3.0.6.tar.gz 274.7 KiB
3.0.6 rolling source/ R- MixtureMissing_3.0.6.tar.gz 57.3 KiB
3.0.6 latest linux/jammy R-4.5 MixtureMissing_3.0.6.tar.gz 275.1 KiB
3.0.6 latest linux/noble R-4.5 MixtureMissing_3.0.6.tar.gz 274.7 KiB
3.0.6 latest source/ R- MixtureMissing_3.0.6.tar.gz 57.3 KiB
3.0.6 2026-04-26 source/ R- MixtureMissing_3.0.6.tar.gz 57.3 KiB
3.0.6 2026-04-23 source/ R- MixtureMissing_3.0.6.tar.gz 57.3 KiB
3.0.6 2026-04-09 windows/windows R-4.5 MixtureMissing_3.0.6.zip 278.6 KiB
3.0.4 2025-04-20 source/ R- MixtureMissing_3.0.4.tar.gz 56.6 KiB

Dependencies (latest)

Imports