Create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering.
|Date of publication||2016-07-14 21:23:54|
|Maintainer||Sam Buttrey <firstname.lastname@example.org>|
|License||GPL (>= 2)|
cramer: Compute Cramer's V for a two-way table
d3.dist: D3-style dissimilarity for a single tree
leaf.numbers: Convert "where" entry of tree frame into leaf numbers
make.leaf.paths: Make matrix of leaf paths
plot.treeClust: Plot treeClust object
print.treeClust: Print treeClust object
rpart.predict.leaves: Return the leaf into which observations are predicted to fall
rp.deviance: Compute deviance within nodes of classification trees
summary.treeClust: Summarize treeClust object
tcdist: Compute treeClust dissimilarities
tcnewdata: Create all-numeric data to mimic the inter-point distances...
treeClust: Build a tree-based dissimilarity for clustering, and...
treeClust.control: Parameters describing the output from a treeClust fit
treeClust.dist: Built treeClust distance
treeClust.rpart: Build an rpart tree as part of treeClust