Package: T4cluster 0.1.4

T4cluster: Tools for Cluster Analysis

Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.

Authors:Kisung You [aut, cre]

T4cluster_0.1.4.tar.gz
T4cluster_0.1.4.zip(r-4.7)T4cluster_0.1.4.zip(r-4.6)T4cluster_0.1.4.zip(r-4.5)
T4cluster_0.1.4.tgz(r-4.6-x86_64)T4cluster_0.1.4.tgz(r-4.6-arm64)T4cluster_0.1.4.tgz(r-4.5-x86_64)T4cluster_0.1.4.tgz(r-4.5-arm64)
T4cluster_0.1.4.tar.gz(r-4.7-arm64)T4cluster_0.1.4.tar.gz(r-4.7-x86_64)T4cluster_0.1.4.tar.gz(r-4.6-arm64)T4cluster_0.1.4.tar.gz(r-4.6-x86_64)
T4cluster_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
T4cluster/json (API)
NEWS

# Install 'T4cluster' in R:

install.packages('T4cluster', repos = c('https://kisungyou.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kisungyou/t4cluster/issues

Pkgdown/docs site:https://kisungyou.com

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

household - Load 'household' data

On CRAN:

openblas cpp openmp

4.54 score 7 stars 3 packages 11 scripts 290 downloads 40 exports 77 dependencies

Last updated from:30b066232b. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	257
linux-devel-x86_64	OK	265
source / vignettes	OK	381
linux-release-arm64	OK	261
linux-release-x86_64	OK	257
macos-release-arm64	OK	149
macos-release-x86_64	OK	340
macos-oldrel-arm64	OK	193
macos-oldrel-x86_64	OK	574
windows-devel	OK	249
windows-release	OK	274
windows-oldrel	OK	267
wasm-release	OK	211

Exports:ccphate compare.adjrand compare.rand dpmeans EKSS ephclust funhclust funkmeans03A gbphate gen3S genDONUTS genLP genSMILEY gmm gmm03F gmm11R gmm16G gskmeans kmeans kmeans18B kmeanspp LRR LRSC LSR MSM pcm psm quality.CH quality.sil sc05Z sc09G sc10Z sc11Y sc12L scNJW scSM scUL spkmeans SSC SSQP

Dependencies:ADMM ash backports bitops checkmate clarabel cli cluster codetools colorspace cpp11 CVXR dbscan deSolve doParallel farver fastcluster fda fds FNN foreach generics ggplot2 glue gmp gsignal gtable hdrcde highs isoband iterators kernlab KernSmooth ks labdsv labeling lattice lifecycle locfit lpSolve maotai MASS Matrix mclust mclustcomp mgcv multicool mvtnorm nlme osqp pcaPP pracma R6 rainbow RANN rbibutils RColorBrewer Rcpp RcppArmadillo RcppDE RcppDist RcppEigen RCurl Rdimtools Rdpack rlang RSpectra rstiefel Rtsne S7 scales scatterplot3d scs slam vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Consensus Clustering with PHATE Geometry	ccphate
(+) Adjusted Rand Index	compare.adjrand
(+) Rand Index	compare.rand
DP-Means Clustering	dpmeans
Ensembles of K-Subspaces	EKSS
Hierarchical Agglomerative Clustering for Empirical Distributions	ephclust
Functional Hierarchical Clustering	funhclust
Functional K-Means Clustering by Abraham et al. (2003)	funkmeans03A
Generalized Bayesian Clustering with PHATE Geometry	gbphate
Generate from Three 5-dimensional Subspaces in 200-dimensional space.	gen3S
Generate Nested Donuts	genDONUTS
Generate Line and Plane Example with Fixed Number of Components	genLP
Generate SMILEY Data	genSMILEY
Finite Gaussian Mixture Model	gmm
Ensemble of Gaussian Mixtures with Random Projection	gmm03F
Regularized GMM by Ruan et al. (2011)	gmm11R
Weighted GMM by Gebru et al. (2016)	gmm16G
Geodesic Spherical K-Means	gskmeans
Load 'household' data	household
K-Means Clustering	kmeans
K-Means Clustering with Lightweight Coreset	kmeans18B
K-Means++ Clustering	kmeanspp
Low-Rank Representation	LRR
Low-Rank Subspace Clustering	LRSC
Least Squares Regression	LSR
Bayesian Mixture of Subspaces of Different Dimensions	MSM
Compute Pairwise Co-occurrence Matrix	pcm
S3 method to predict class label of new data with 'MSM' object	predict.MSM
Compute Posterior Similarity Matrix	psm
(+) CH index	quality.CH
(+) Silhouette Index	quality.sil
Spectral Clustering by Zelnik-Manor and Perona (2005)	sc05Z
Spectral Clustering by Gu and Wang (2009)	sc09G
Spectral Clustering by Zhang et al. (2010)	sc10Z
Spectral Clustering by Yang et al. (2011)	sc11Y
Spectral Clustering by Li and Guo (2012)	sc12L
Spectral Clustering by Ng, Jordan, and Weiss (2002)	scNJW
Spectral Clustering by Shi and Malik (2000)	scSM
Spectral Clustering with Unnormalized Laplacian	scUL
Spherical K-Means Clustering	spkmeans
Sparse Subspace Clustering	SSC
Subspace Segmentation via Quadratic Programming	SSQP