Package: PPCI 0.1.5

David Hofmeyr
PPCI: Projection Pursuit for Cluster Identification
Implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <https://jmlr.csail.mit.edu/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Authors:
PPCI_0.1.5.tar.gz
PPCI_0.1.5.zip(r-4.5)PPCI_0.1.5.zip(r-4.4)PPCI_0.1.5.zip(r-4.3)
PPCI_0.1.5.tgz(r-4.5-x86_64)PPCI_0.1.5.tgz(r-4.5-arm64)PPCI_0.1.5.tgz(r-4.4-x86_64)PPCI_0.1.5.tgz(r-4.4-arm64)PPCI_0.1.5.tgz(r-4.3-x86_64)PPCI_0.1.5.tgz(r-4.3-arm64)
PPCI_0.1.5.tar.gz(r-4.5-noble)PPCI_0.1.5.tar.gz(r-4.4-noble)
PPCI_0.1.5.tgz(r-4.4-emscripten)PPCI_0.1.5.tgz(r-4.3-emscripten)
PPCI.pdf |PPCI.html✨
PPCI/json (API)
# Install 'PPCI' in R: |
install.packages('PPCI', repos = c('https://davidhofmeyr.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/davidhofmeyr/ppci/issues0 issues
- breastcancer - Discrimination of Cancerous and Non-Cancerous Breast Masses
- dermatology - Eryhemato-Squamous Disease Identification
- optidigits - Optical Recognition of Handwritten Digits
- pendigits - Pen-based Recognition of Handwritten Digits
- phoneme - Speech Recognition through Phoneme Identification
- yale - Face Recognition
On CRAN:PPCI-0.1.5(2020-03-06)
Last updated 5 years agofrom:aa8fa12f6c. Checks:1 OK, 10 WARNING. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Feb 11 2025 |
R-4.5-win-x86_64 | WARNING | Feb 11 2025 |
R-4.5-mac-x86_64 | WARNING | Feb 11 2025 |
R-4.5-mac-aarch64 | WARNING | Feb 11 2025 |
R-4.5-linux-x86_64 | WARNING | Feb 11 2025 |
R-4.4-win-x86_64 | WARNING | Feb 11 2025 |
R-4.4-mac-x86_64 | WARNING | Feb 11 2025 |
R-4.4-mac-aarch64 | WARNING | Feb 11 2025 |
R-4.3-win-x86_64 | WARNING | Feb 11 2025 |
R-4.3-mac-x86_64 | WARNING | Feb 11 2025 |
R-4.3-mac-aarch64 | WARNING | Feb 11 2025 |
Exports:add_subtreecluster_performancedf_mcdf_mddf_md_cppdf_ncutdncut_xf_mcf_mdf_md_cppf_ncuthp_plotis_minimismin_cppmc_bmcdcmcdrmchmcppmd_bmd_b_cppmd_reldepthmddcmddrmdhmdppncut_bncut_xncutdcncutdrncuthncutppnode_plotnorm_vecoptidigits_mean_imagesplot.ppci_cluster_solutionplot.ppci_hyperplane_solutionplot.ppci_projection_solutionppclust.optimsubtree_widthsuccess_ratiotree_plottree_prunetree_split
Dependencies:latticeMatrixrARPACKRcppRcppArmadilloRcppEigenRSpectra
Citation
The following are references to the package. You should also reference the individual methods used, as detailed in the reference section of the help files for each function.
Hofmeyr DP, Pavlidis NG (2019). “PPCI: an R Package for Cluster Identification using Projection Pursuit.” The R Journal. doi:10.32614/RJ-2019-046, https://journal.r-project.org/archive/2019/RJ-2019-046/index.html.
To get Bibtex entries use: x<-citation("PPCI"); toBibtex(x)
Corresponding BibTeX entry:
@Article{, title = {{PPCI}: an {R} Package for Cluster Identification using Projection Pursuit}, author = {David P. Hofmeyr and Nicos G. Pavlidis}, journal = {{The R Journal}}, year = {2019}, doi = {10.32614/RJ-2019-046}, url = {https://journal.r-project.org/archive/2019/RJ-2019-046/index.html}, }
Readme and manuals
PPCI
An R Package for Cluster Identification using Projection Pursuit To install from R console:
library(devtools)
install_github("DavidHofmeyr/PPCI")
library(PPCI)