Package: gmmsslm 1.1.5
gmmsslm: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism
The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.
Authors:
gmmsslm_1.1.5.tar.gz
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gmmsslm_1.1.5.tgz(r-4.4-any)gmmsslm_1.1.5.tgz(r-4.3-any)
gmmsslm_1.1.5.tar.gz(r-4.5-noble)gmmsslm_1.1.5.tar.gz(r-4.4-noble)
gmmsslm_1.1.5.tgz(r-4.4-emscripten)gmmsslm_1.1.5.tgz(r-4.3-emscripten)
gmmsslm.pdf |gmmsslm.html✨
gmmsslm/json (API)
# Install 'gmmsslm' in R: |
install.packages('gmmsslm', repos = c('https://lyu9118.r-universe.dev', 'https://cloud.r-project.org')) |
- gastro_data - Gastrointestinal dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:41a58bb54a. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 15 2024 |
R-4.5-win | OK | Oct 15 2024 |
R-4.5-linux | OK | Oct 15 2024 |
R-4.4-win | OK | Oct 15 2024 |
R-4.4-mac | OK | Oct 15 2024 |
R-4.3-win | OK | Oct 15 2024 |
R-4.3-mac | OK | Oct 15 2024 |
Exports:bayesclassifiercov2vecdiscriminant_betaerateget_clusterprobsget_entropygmmsslminitialvaluelist2parloglk_fullloglk_igloglk_misslogsumexpmakelabelmatrixneg_objective_functionnormalise_logprobpar2listparaextractplot_missingnesspredictpro2vecrlabelrmixsummaryvec2covvec2pro
Dependencies:mvtnorm