Package: MixtureMissing 3.0.3
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. 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.
Authors:
MixtureMissing_3.0.3.tar.gz
MixtureMissing_3.0.3.zip(r-4.5)MixtureMissing_3.0.3.zip(r-4.4)MixtureMissing_3.0.3.zip(r-4.3)
MixtureMissing_3.0.3.tgz(r-4.4-any)MixtureMissing_3.0.3.tgz(r-4.3-any)
MixtureMissing_3.0.3.tar.gz(r-4.5-noble)MixtureMissing_3.0.3.tar.gz(r-4.4-noble)
MixtureMissing_3.0.3.tgz(r-4.4-emscripten)MixtureMissing_3.0.3.tgz(r-4.3-emscripten)
MixtureMissing.pdf |MixtureMissing.html✨
MixtureMissing/json (API)
# Install 'MixtureMissing' in R: |
install.packages('MixtureMissing', repos = c('https://hungtong.r-universe.dev', 'https://cloud.r-project.org')) |
- UScost - US Cost of Living Indices in 2019 Data Set
- auto - Automobile Data Set
- bankruptcy - Bankruptcy Data Set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:3631c5dfa0. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 18 2024 |
R-4.5-win | OK | Nov 18 2024 |
R-4.5-linux | OK | Nov 18 2024 |
R-4.4-win | OK | Nov 18 2024 |
R-4.4-mac | OK | Nov 18 2024 |
R-4.3-win | OK | Nov 18 2024 |
R-4.3-mac | OK | Nov 18 2024 |
Exports:evaluation_metricsextractgenerate_patternshide_valuesinitialize_clustersMCNMmean_imputeMGHMselect_mixture
Dependencies:backportsBesselbitbit64bootbroomclicliprclustercodetoolscpp11crayondplyrfansiforcatsforeachgenericsglmnetgluegmphavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmclustmiceminqamitmlmnormtmvtnormnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6RcppRcppEigenreadrrlangRmpfrrpartshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Automobile Data Set | auto |
Bankruptcy Data Set | bankruptcy |
Binary Classification Evaluation | evaluation_metrics |
Extractor function for MixtureMissing | extract |
Missing-Data Pattern Generation | generate_patterns |
Missing Values Generation | hide_values |
Cluster Initialization using a Heuristic Method | initialize_clusters |
Multivariate Contaminated Normal Mixture (MCNM) | MCNM |
Mean Imputation | mean_impute |
Multivariate Generalized Hyperbolic Mixture (MGHM) | MGHM |
MixtureMissing Plotting | plot.MixtureMissing |
Print for MixtureMissing | print.MixtureMissing |
Mixture Model Selection | select_mixture |
Summary for MixtureMissing | summary.MixtureMissing |
US Cost of Living Indices in 2019 Data Set | UScost |