Package: meteorits 0.1.1.9000
meteorits: Mixture-of-Experts Modeling for Complex Non-Normal Distributions
Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F. (2015) <arxiv:1506.06707>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>. Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>. Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.
Authors:
meteorits_0.1.1.9000.tar.gz
meteorits_0.1.1.9000.zip(r-4.5)meteorits_0.1.1.9000.zip(r-4.4)meteorits_0.1.1.9000.zip(r-4.3)
meteorits_0.1.1.9000.tgz(r-4.4-x86_64)meteorits_0.1.1.9000.tgz(r-4.4-arm64)meteorits_0.1.1.9000.tgz(r-4.3-x86_64)meteorits_0.1.1.9000.tgz(r-4.3-arm64)
meteorits_0.1.1.9000.tar.gz(r-4.5-noble)meteorits_0.1.1.9000.tar.gz(r-4.4-noble)
meteorits_0.1.1.9000.tgz(r-4.4-emscripten)meteorits_0.1.1.9000.tgz(r-4.3-emscripten)
meteorits.pdf |meteorits.html✨
meteorits/json (API)
NEWS
# Install 'meteorits' in R: |
install.packages('meteorits', repos = c('https://fchamroukhi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fchamroukhi/meteorits/issues
- tempanomalies - Global Annual Temperature Anomalies
artificial-intelligenceclusteringem-algorithmmixture-of-expertsneural-networksnon-linear-regressionpredictionrobust-learningskew-normalskew-tskewed-datastatistical-inferencestatistical-learningt-distributionunsupervised-learning
Last updated 5 years agofrom:11c42aa94a. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 11 2024 |
R-4.5-win-x86_64 | NOTE | Nov 11 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 11 2024 |
R-4.4-win-x86_64 | NOTE | Nov 11 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 11 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 11 2024 |
R-4.3-win-x86_64 | NOTE | Nov 11 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 11 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 11 2024 |
Exports:emNMoEemSNMoEemStMoEemTMoEsampleUnivNMoEsampleUnivSNMoEsampleUnivStMoEsampleUnivTMoE
Dependencies:MASSpracmaRcppRcppArmadillo
A-quick-tour-of-NMoE
Rendered fromA-quick-tour-of-NMoE.Rmd
usingknitr::rmarkdown
on Nov 11 2024.Last update: 2019-09-20
Started: 2019-07-16
A-quick-tour-of-SNMoE
Rendered fromA-quick-tour-of-SNMoE.Rmd
usingknitr::rmarkdown
on Nov 11 2024.Last update: 2019-09-20
Started: 2019-07-11
A-quick-tour-of-StMoE
Rendered fromA-quick-tour-of-StMoE.Rmd
usingknitr::rmarkdown
on Nov 11 2024.Last update: 2019-09-20
Started: 2019-07-11
A-quick-tour-of-tMoE
Rendered fromA-quick-tour-of-tMoE.Rmd
usingknitr::rmarkdown
on Nov 11 2024.Last update: 2019-09-20
Started: 2019-07-11