Package: mccca 2.4

Mariko Takagishi

mccca: Visualizing Class Specific Heterogeneous Tendencies in Categorical Data

Provides functions for performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the package are described in Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.

Authors:Mariko Takagishi [aut, cre]

mccca_2.4.tar.gz
mccca_2.4.zip(r-4.7-x86_64)mccca_2.4.zip(r-4.7-arm64)mccca_2.4.zip(r-4.6-x86_64)mccca_2.4.zip(r-4.6-arm64)
mccca_2.4.tar.gz(r-4.7-arm64)mccca_2.4.tar.gz(r-4.7-x86_64)mccca_2.4.tar.gz(r-4.6-arm64)mccca_2.4.tar.gz(r-4.6-x86_64)
mccca_2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mccca/json (API)

# Install 'mccca' in R:
install.packages('mccca', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.30 score 358 downloads 5 exports 37 dependencies

Last updated from:66b4cb263a. Checks:10 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK162
linux-devel-x86_64OK195
source / vignettesOK242
linux-release-arm64OK200
linux-release-x86_64OK186
windows-devel-arm64OK342
windows-devel-x86_64OK219
windows-release-arm64OK306
windows-release-x86_64OK156
wasm-releaseOK157

Exports:create.MCCCAdatadecideKgenerate.extgenerate.onedataMCCCA

Dependencies:abindade4classcliclusterclusterSimcolorspacecpp11e1071farverggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagicmagrittrMASSpixmapproxyR6RColorBrewerRcppRcppArmadillorlangS7scalesspstringistringrvctrsviridisLitewithrwordcloud