sdols v1.3


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Summarizing Distributions of Latent Structures

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.

Functions in sdols

Name Description
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
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Type Package
Date 2017-12-01
LazyData TRUE
License Apache License 2.0 | file LICENSE
Encoding UTF-8
RoxygenNote 6.0.1
NeedsCompilation no
Packaged 2017-12-01 13:17:56 UTC; dahl
Repository CRAN
Date/Publication 2017-12-01 18:54:16 UTC

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