Package: Evacluster 0.1.0
Evacluster: Evaluation Clustering Methods for Disease Subtypes Diagnosis
Contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021)<doi:10.1016/j.compbiomed.2021.104753>, and Fahimeh Nezhadmoghadam, et al.(2021)<doi:10.2174/1567205018666210831145825>.
Authors:
Evacluster_0.1.0.tar.gz
Evacluster_0.1.0.zip(r-4.7-any)Evacluster_0.1.0.zip(r-4.6-any)Evacluster_0.1.0.zip(r-4.5-any)
Evacluster_0.1.0.tgz(r-4.6-any)Evacluster_0.1.0.tgz(r-4.5-any)
Evacluster_0.1.0.tar.gz(r-4.7-any)Evacluster_0.1.0.tar.gz(r-4.6-any)
Evacluster_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Evacluster/json (API)
| # Install 'Evacluster' in R: |
| install.packages('Evacluster', repos = c('https://fahimehnm.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:e35fbae9b4. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 180 | ||
| source / vignettes | OK | 231 | ||
| linux-release | NOTE | 150 | ||
| macos-release | NOTE | 152 | ||
| macos-oldrel | NOTE | 211 | ||
| windows-devel | NOTE | 93 | ||
| windows-release | NOTE | 86 | ||
| windows-oldrel | NOTE | 91 | ||
| wasm-release | OK | 111 |
Exports:clusterStabilityEMClusterFuzzyClustergetConsensusClusterhierarchicalClusterkmeansClusternmfClusterpamClusterpredict.EMClusterpredict.FuzzyClusterpredict.hierarchicalClusterpredict.kmeansClusterpredict.nmfClusterpredict.pamClusterpredict.tsneReductortsneReductor
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Evaluation Clustering Methods for Disease Subtypes Diagnosis (Evacluster) | Evacluster-package Evacluster |
| clustering stability function | clusterStability |
| Expectation Maximization Clustering | EMCluster |
| Fuzzy C-means Clustering Algorithm | FuzzyCluster |
| Consensus Clustering Results | getConsensusCluster |
| hierarchical clustering | hierarchicalCluster |
| K-means Clustering | kmeansCluster |
| Non-negative matrix factorization (NMF) | nmfCluster |
| Partitioning Around Medoids (PAM) Clustering | pamCluster |
| EMCluster prediction function | predict.EMCluster |
| FuzzyCluster prediction function | predict.FuzzyCluster |
| hierarchicalCluster prediction function | predict.hierarchicalCluster |
| kmeansCluster prediction function | predict.kmeansCluster |
| nmfCluster prediction function | predict.nmfCluster |
| pamCluster prediction function | predict.pamCluster |
| tsneReductor prediction function | predict.tsneReductor |
| t-Distributed Stochastic Neighbor Embedding (t-SNE) | tsneReductor |