Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random R package MixtureMissing version 3.0.6 CRAN Task View: Cluster (https://CRAN.R-project.org/view=Cluster) CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) 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. Software Depends: R (>= 3.5.0) Imports: mvtnorm (>= 1.1-2), mnormt (>= 2.0.2), cluster (>= 2.1.2), MASS (>= 7.3), numDeriv (>= 8.1.1), Bessel (>= 0.6.0), mclust (>= 5.0.0), mice (>= 3.10.0) Hung Tong <hungtongmx@gmail.com> Comprehensive R Archive Network (CRAN) Hung Tong [aut, cre], Cristina Tortora [aut, ths, dgs] GPL (>= 2) 2026-01-14 application/tgz https://CRAN.R-project.org/package=MixtureMissing doi:10.32614/CRAN.package.MixtureMissing