Package: MixtureMissing
Type: Package
Title: Robust Model-Based Clustering for Data Sets with Missing Values
        at Random
Version: 1.0.2
Authors@R: 
  c(person(given = "Hung",
           family = "Tong",
           role = c("aut", "cre"),
           email = "hungtongmx@gmail.com"),
    person(given = "Cristina",
           family = "Tortora",
           role = c("aut", "ths", "dgs"),
           email = "cristina.tortora@sjsu.edu"))
Description: Implementation of robust model based cluster analysis with missing data. 
    The models used are: Multivariate Contaminated Normal Mixtures (MCNM),
    Multivariate Student's t  Mixtures (MtM), and Multivariate Normal Mixtures (MNM)
    for data sets with missing values at random. 
    See "Model-Based Clustering and Outlier Detection with Missing Data" by
    Hung Tong and Cristina Tortora (2022) <doi:10.1007/s11634-021-00476-1>.
Imports: ContaminatedMixt (>= 1.3.4.1), mvtnorm (>= 1.1-2), mnormt (>=
        2.0.2), cluster (>= 2.1.2), rootSolve (>= 1.8.2.2), MASS (>=
        7.3)
Suggests: mice (>= 3.10.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Repository: CRAN
RoxygenNote: 7.1.2
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2022-01-29 21:04:56 UTC; hmtong
Author: Hung Tong [aut, cre],
  Cristina Tortora [aut, ths, dgs]
Maintainer: Hung Tong <hungtongmx@gmail.com>
Date/Publication: 2022-01-30 23:00:04 UTC
