Package: MGMM 1.0.1.2
MGMM: Missingness Aware Gaussian Mixture Models
Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.
Authors:
MGMM_1.0.1.2.tar.gz
MGMM_1.0.1.2.zip(r-4.5)MGMM_1.0.1.2.zip(r-4.4)MGMM_1.0.1.2.zip(r-4.3)
MGMM_1.0.1.2.tgz(r-4.4-x86_64)MGMM_1.0.1.2.tgz(r-4.4-arm64)MGMM_1.0.1.2.tgz(r-4.3-x86_64)MGMM_1.0.1.2.tgz(r-4.3-arm64)
MGMM_1.0.1.2.tar.gz(r-4.5-noble)MGMM_1.0.1.2.tar.gz(r-4.4-noble)
MGMM_1.0.1.2.tgz(r-4.4-emscripten)MGMM_1.0.1.2.tgz(r-4.3-emscripten)
MGMM.pdf |MGMM.html✨
MGMM/json (API)
NEWS
| # Install 'MGMM' in R: |
| install.packages('MGMM', repos = c('https://zrmacc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zrmacc/mgmm/issues
Last updated 3 months agofrom:6c9c66910f. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 01 2024 |
| R-4.5-win-x86_64 | OK | Sep 01 2024 |
| R-4.5-linux-x86_64 | OK | Sep 01 2024 |
| R-4.4-win-x86_64 | OK | Sep 01 2024 |
| R-4.4-mac-x86_64 | OK | Sep 01 2024 |
| R-4.4-mac-aarch64 | OK | Sep 01 2024 |
| R-4.3-win-x86_64 | OK | Sep 01 2024 |
| R-4.3-mac-x86_64 | OK | Sep 01 2024 |
| R-4.3-mac-aarch64 | OK | Sep 01 2024 |
Exports:ChooseKClustQualCombineMIsFitGMMGenImputationPartitionDataReconstituteDatarGMM
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calinski-Harabaz Index | CalHar |
| Cluster Number Selection | ChooseK |
| Cluster Quality | ClustQual |
| Combine Multiple Imputations | CombineMIs |
| Davies-Bouldin Index | DavBou |
| Estimate Multivariate Normal Mixture | FitGMM |
| Fit Multivariate Mixture Distribution | FitMix |
| Fit Multivariate Normal Distribution | FitMVN |
| Generate Imputation | GenImputation |
| Log likelihood for Fitted GMM | logLik.mix |
| Log likelihood for Fitted MVN Model | logLik.mvn |
| Mean for Fitted GMM | mean.mix |
| Mean for Fitted MVN Model | mean.mvn |
| Mixture Model Class | mix-class |
| Mean Update for Mixture of MVNs with Missingness. | MixUpdateMeans |
| Multivariate Normal Model Class | mvn-class |
| Partition Data by Missingness Pattern | PartitionData |
| Print for Fitted GMM | print.mix |
| Print for Fitted MVN Model | print.mvn |
| Reconstitute Data | ReconstituteData |
| Generate Data from Gaussian Mixture Models | rGMM |
| Show for Fitted Mixture Models | show,mix-method |
| Show for Multivariate Normal Models | show,mvn-method |
| Covariance for Fitted GMM | vcov.mix |
| Covariance for Fitted MVN Model | vcov.mvn |
