optpart3.0-3 package

Optimal Partitioning of Similarity Relations

tabdev

Classification Validity Assessment by Table Deviance

testpart

Identify Misclassified Plots in a Partition

typal

Identification of Typal Samples in a Partition

archi

Archipelago Analysis

bestfit

Identify the Goodness-of-Fit of Cluster Members

bestopt

Best Of Set Optimal Partitions From Random Starts

classmatch

Classification Matching and Differencing

clique

Maximal Clique Analysis

clique.test

Clique Test

clustering

Clustering Object

compare

Compare Species Constancy for Specified Clusters

confus

(Fuzzy) Confusion Matrix

consider

Recommendations for Possible Merging of Clusters

disdiam

Dissimilarity Diameters of a Partition

extract

Extract A Specific Cluster Solution From A Stride

flexbeta

Calculate a Flexible-Beta Dendrogram

gensilwidth

Generalized Silhouette Width

lambda

Goodman- Kruskal Lambda Index of Classification Association

maxsimset

Maximally Similar Sets Analysis

mergeclust

Merge Specified Clusters in a Classification

murdoch

Indicator Species Analysis by Murdoch Preference Function

neighbor

Neighbor Analysis of Partitions

optimclass

Optimum Classification by Counts of Indicator Species

optindval

Optimizing Classification by Maximizing Dufrene and Legendre's Indicat...

optpart.internal

Internal Optpart Functions

optpart

Optimal Partitioning of Dissimilarity/Distance Matrices

optsil

Clustering by Optimizing Silhouette Widths

opttdev

Optimizing Classification by Minimizing Table Deviance

partana

Partition Analysis

partition

Convert Object to Partition Object

phi

Calculating the phi Statistic on Taxon Classifications

refine

Refining a Classification by Re-Assigning Memberships

reordclust

Re-order Clusters in a Classification

silhouette

Produce a Silhouette Object From a Partana, Clustering, or Stride Obje...

slice

Slice a Hierarchical Clustering Dendrogram with a Mouse

stride

Stride: Producing a Sequence of Clusterings

Contains a set of algorithms for creating partitions and coverings of objects largely based on operations on (dis)similarity relations (or matrices). There are several iterative re-assignment algorithms optimizing different goodness-of-clustering criteria. In addition, there are covering algorithms 'clique' which derives maximal cliques, and 'maxpact' which creates a covering of maximally compact sets. Graphical analyses and conversion routines are also included.