Package: SurvivalClusteringTree 1.0

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.

Authors:Lu You [aut, cre], Lauric Ferrat [aut], Hemang Parikh [aut], Yanan Huo [aut], Jeffrey Krischer [ctb], Maria Redondo [ctb], Richard Oram [ctb], Andrea Steck [ctb]

SurvivalClusteringTree_1.0.tar.gz
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SurvivalClusteringTree_1.0.tgz(r-4.4-x86_64)SurvivalClusteringTree_1.0.tgz(r-4.4-arm64)SurvivalClusteringTree_1.0.tgz(r-4.3-x86_64)SurvivalClusteringTree_1.0.tgz(r-4.3-arm64)
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SurvivalClusteringTree.pdf |SurvivalClusteringTree.html✨
SurvivalClusteringTree/json (API)

Peer review:

Bug tracker:https://github.com/luyouepiusf/survivalclusteringtree/issues

On CRAN:

10 exports 0.62 score 33 dependencies 2 scripts 167 downloads

Last updated 1 years agofrom:621fb090ed. Checks:OK: 9. Indexed: yes.

Exports:plot_survival_treepredict_distance_forestpredict_distance_forest_matrixpredict_distance_treepredict_distance_tree_matrixpredict_weightssurvival_forestsurvival_forest_matrixsurvival_treesurvival_tree_matrix

Dependencies:clicommonmarkcurldplyrfansiformula.toolsgenericsgluegridtextjpeglatticelifecyclemagrittrmarkdownMatrixoperator.toolspillarpkgconfigpngR6RcppRcppArmadillorlangstringistringrsurvivaltibbletidyselectutf8vctrswithrxfunxml2