Depends: R (>= 3.5.0), stats, utils, graphics, nlme, Rcpp (>= 0.12.3)
Imports: rgl, ellipse, numDeriv, ggplot2, ggpubr, foreach, iterators,

Title: Clustering on the Torus by Conformal Prediction
Description: Provides various tools of for clustering multivariate angular
data on the torus. The package provides angular
adaptations of usual clustering methods such as the k-means
clustering, pairwise angular distances, which can be used as an
input for distance-based clustering algorithms, and implements
clustering based on the conformal prediction framework. Options
for the conformal scores include scores based on a kernel density
estimate, multivariate von Mises mixtures, and naive k-means clusters.
Moreover, the package provides some basic data handling tools for
c(person("Sungkyu", "Jung", email = "sungkyu@snu.ac.kr", role = c("aut","cph")),
person("Seungki", "Hong", email = "skgaboja@snu.ac.kr", role = c("aut", "cre")),
person("Kiho", "Park", role = c("ctb")),
person("Byungwon", "Kim", role = c("ctb")))
Imports: BAMBI, igraph, purrr, ggplot2, rlang
Suggests: knitr, rmarkdown, tidyverse, cowplot
Packaged: 2021-02-05 17:05:47 UTC; hongseung-gi

Imports: BAMBI, igraph, purrr, ggplot2, rlang, stats, utils
Packaged: 2021-06-30 06:06:43 UTC; hongseung-gi

Title: Prediction and Clustering on the Torus by Conformal Prediction
Packaged: 2021-07-26 08:35:59 UTC; hongseung-gi

Imports: BAMBI, igraph, purrr, ggplot2, rlang, stats, utils, cowplot
Packaged: 2021-12-16 12:43:29 UTC; hongseung-gi

Packaged: 2021-12-17 00:48:39 UTC; hongseung-gi

Packaged: 2022-01-04 01:58:20 UTC; hongseung-gi

Title: Gaussian-Based Visualization of Generic Model-Based Clustering
Authors@R: c(person("Christophe", "Biernacki", email = "christophe.biernacki@math.univ-lille1.fr", role = c("aut")), person("Matthieu", "Marbac", email = "matthieu.marbac@gmail.com", role = c("aut", "cre")), person("Vincent", "Vandewalle", email = "vincent.vandewalle@inria.fr", role = c("aut")))
Description: Gaussian-Based Visualization of clustering achieved by mixture model on any type of data. Visualization is based on the probabilities of classification.
Imports: Rcpp, MASS, parallel, mgcv, mvtnorm, Rmixmod
Collate: 'clusvis.R' 'estimation.R' 'smartinit.R' 'RcppExports.R'