Package: SurvivalClusteringTree 1.1.1
SurvivalClusteringTree: Clustering Analysis Using Survival Tree and Forest Algorithms
An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.
Authors:
SurvivalClusteringTree_1.1.1.tar.gz
SurvivalClusteringTree_1.1.1.tar.gz(r-4.5-noble)SurvivalClusteringTree_1.1.1.tar.gz(r-4.4-noble)
SurvivalClusteringTree_1.1.1.tgz(r-4.4-emscripten)SurvivalClusteringTree_1.1.1.tgz(r-4.3-emscripten)
SurvivalClusteringTree.pdf |SurvivalClusteringTree.html✨
SurvivalClusteringTree/json (API)
# Install 'SurvivalClusteringTree' in R: |
install.packages('SurvivalClusteringTree', repos = c('https://cran.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 7 months agofrom:d74f63e1aa. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 21 2024 |
R-4.5-linux-x86_64 | OK | Dec 21 2024 |
Exports:plot_survival_treepredict_distance_forestpredict_distance_forest_matrixpredict_distance_treepredict_distance_tree_matrixpredict_weightspredict_weights_matrixsurvival_forestsurvival_forest_matrixsurvival_treesurvival_tree_matrix
Dependencies:clicommonmarkcurldplyrfansiformula.toolsgenericsgluegridtextjpeglatticelifecyclemagrittrmarkdownMatrixoperator.toolspillarpkgconfigpngR6RcppRcppArmadillorlangstringistringrsurvivaltibbletidyselectutf8vctrswithrxfunxml2