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sdols (version 1.3)

Summarizing Distributions of Latent Structures

Description

Summaries of distributions on clusterings and feature allocations are provided. Specifically, point estimates are obtained by the sequentially-allocated latent structure optimization (SALSO) algorithm to minimize squared error loss, absolute error loss, Binder loss, or the lower bound of the variation of information loss. Clustering uncertainty can be assessed with the confidence calculations and the associated plot.

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Version

Install

install.packages('sdols')

Monthly Downloads

21

Version

1.3

License

Apache License 2.0 | file LICENSE

Maintainer

David B Dahl

Last Published

December 1st, 2017

Functions in sdols (1.3)

iris.clusterings

Clusterings of the Iris Data
dlso

Perform Draws-Based Latent Structure Optimization
USArrests.featureAllocations

Feature Allocations of the USArrests Dataset
expectedPairwiseAllocationMatrix

Compute Expected Pairwise Allocation Matrix
confidence

Compute Clustering Confidence
plot.sdols.confidence

Confidence and Exemplar Plotting
salso

Perform Sequentially-Allocated Latent Structure Optimization
scalaConvert.featureAllocation

(Developers Only:) Convert Between R and Scala Representations of Feature Allocations
latentStructureFit

Compute Fit Summaries for a Latent Structure Estimate