Package: tclust 2.0-4
tclust: Robust Trimmed Clustering
Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011) <doi:10.1007/s11222-010-9194-z> and others.
Authors:
tclust_2.0-4.tar.gz
tclust_2.0-4.zip(r-4.5)tclust_2.0-4.zip(r-4.4)tclust_2.0-4.zip(r-4.3)
tclust_2.0-4.tgz(r-4.4-x86_64)tclust_2.0-4.tgz(r-4.4-arm64)tclust_2.0-4.tgz(r-4.3-x86_64)tclust_2.0-4.tgz(r-4.3-arm64)
tclust_2.0-4.tar.gz(r-4.5-noble)tclust_2.0-4.tar.gz(r-4.4-noble)
tclust_2.0-4.tgz(r-4.4-emscripten)tclust_2.0-4.tgz(r-4.3-emscripten)
tclust.pdf |tclust.html✨
tclust/json (API)
# Install 'tclust' in R: |
install.packages('tclust', repos = c('https://valentint.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/valentint/tclust/issues
Last updated 4 months agofrom:6d0eb288cb. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 06 2024 |
R-4.5-win-x86_64 | OK | Sep 06 2024 |
R-4.5-linux-x86_64 | OK | Sep 06 2024 |
R-4.4-win-x86_64 | OK | Sep 06 2024 |
R-4.4-mac-x86_64 | OK | Sep 06 2024 |
R-4.4-mac-aarch64 | OK | Sep 06 2024 |
R-4.3-win-x86_64 | OK | Sep 06 2024 |
R-4.3-mac-x86_64 | OK | Sep 06 2024 |
R-4.3-mac-aarch64 | OK | Sep 06 2024 |
Exports:ctlcurvesDiscrFactplot.ctlcurvesplot.DiscrFactplot.rlgplot.tclustplot.tkmeansprint.ctlcurvesprint.DiscrFactprint.tclustprint.tkmeansrlgsimula.rlgsimula.tclustsummary.DiscrFacttclusttkmeans
Dependencies:codetoolsdoParallelforeachiteratorsMASSRcppRcppArmadillo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Classification Trimmed Likelihood Curves | ctlcurves print.ctlcurves |
Discriminant Factor analysis for 'tclust' objects | DiscrFact print.DiscrFact |
Old Faithful Geyser Data | geyser2 |
LG5data data | LG5data |
M5data data | M5data |
Pinus nigra dataset | pine |
The 'plot' method for objects of class 'ctlcurves' | plot.ctlcurves |
The 'plot' method for objects of class 'DiscrFact' | plot.DiscrFact |
Plot an 'rlg' object | plot.rlg |
Plot Method for 'tclust' and 'tkmeans' Objects | plot.tclust plot.tkmeans |
Robust Linear Grouping | rlg |
Simulate contaminated data set for applying rlg | simula.rlg |
Simulate contaminated data set for applying TCLUST | simula.tclust |
The 'summary' method for objects of class 'DiscrFact' | summary.DiscrFact |
Swiss banknotes data | swissbank |
TCLUST method for robust clustering | print.tclust tclust |
TKMEANS method for robust K-means clustering | print.tkmeans tkmeans |
Wholesale customers dataset | wholesale |