Statistics package: Difference between revisions

! Description

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| Define clusters from an agglomerative hierarchical cluster tree.

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| Wrapper function for 'linkage' and 'cluster'.

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| Classical multidimensional scaling of a matrix.

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| Compute a confusion matrix for classification problems.

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| Compute the cophenetic correlation coefficient.

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| Create a clustering evaluation object to find the optimal number of clusters.

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| Compute the inconsistency coefficient for each link of a hierarchical cluster tree.

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| Perform a K-means clustering of an NxD matrix.

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| Produce a hierarchical clustering dendrogram.

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| Mahalanobis' D-square distance.

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| Draws NSAMPLES samples from a target stationary distribution PDF using Metropolis-Hastings algorithm.

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| Compute the optimal leaf ordering of a hierarchical binary cluster tree.

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| Return the distance between any two rows in X.

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| Compute pairwise distance between two sets of vectors.

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| Draws NSAMPLES samples from a target stationary distribution PDF using slice sampling of Radford M. Neal.

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| Interchange between distance matrix and distance vector formats.

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