Package: RMBC 0.1.0
RMBC: Robust Model Based Clustering
A robust clustering algorithm (Model-Based) similar to Expectation Maximization for finite mixture normal distributions is implemented, its main advantage is that the estimator is resistant to outliers, that means that results of parameter estimation are still correct when there are atypical values in the sample (see Gonzalez, Maronna, Yohai and Zamar (2021) <https://arxiv.org/abs/2102.06851>).
Authors:
RMBC_0.1.0.tar.gz
RMBC_0.1.0.zip(r-4.5)RMBC_0.1.0.zip(r-4.4)RMBC_0.1.0.zip(r-4.3)
RMBC_0.1.0.tgz(r-4.4-any)RMBC_0.1.0.tgz(r-4.3-any)
RMBC_0.1.0.tar.gz(r-4.5-noble)RMBC_0.1.0.tar.gz(r-4.4-noble)
RMBC_0.1.0.tgz(r-4.4-emscripten)RMBC_0.1.0.tgz(r-4.3-emscripten)
RMBC.pdf |RMBC.html✨
RMBC/json (API)
| # Install 'RMBC' in R: |
| install.packages('RMBC', repos = c('https://jdgonzalezwork.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jdgonzalezwork/rmbc/issues
- phytoplankton_acoustic_data - Phytoplankton_acoustic_data
Last updated 3 years agofrom:4fe44a6e77. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 01 2024 |
| R-4.5-win | NOTE | Sep 01 2024 |
| R-4.5-linux | NOTE | Sep 01 2024 |
| R-4.4-win | NOTE | Sep 01 2024 |
| R-4.4-mac | NOTE | Sep 01 2024 |
| R-4.3-win | OK | Sep 01 2024 |
| R-4.3-mac | OK | Sep 01 2024 |
Dependencies:cellWiseclicolorspaceDEoptimRfansifarverggplot2gluegridExtraGSEgtableisobandktaucenterslabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnlmepcaPPpillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserrcovscalesshapestringistringrsvdtibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| is_in_gr | is_in_gr |
| klfor2normals Compute the Kullback-Leibler divergence for 2 normal multivariate distributions | klfor2normals |
| Phytoplankton_acoustic_data | phytoplankton_acoustic_data |
| quad_disc | quad_disc |
| Robust Model Base Clustering a robust and efficient version of EM algorithm. | RMBC |
| RMBCaux | RMBCaux |
| robustINIT | robustINIT |
| sumkl The sum of K-L divergence measure between two successive iterations for each component of a mixture distribution, | sumkl |
| weightedMscale the M scale of an univariate sample (see reference below) | weightedMscale |
| weightedSestimator | weightedSestimator |
| weightW | weightW |
