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DPCD: Dirichlet Process Clustering with Dissimilarities

A Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. The latent configuration of objects and the number of clusters are automatically inferred during the fitting process. The package supports multiple models which are available to detect clusters of various shapes and sizes using different covariance structures. Additional functions are included to ensure adequate model fits through prior and posterior predictive checks.

Version: 0.0.1
Depends: nimble, R (≥ 3.5)
Imports: ggplot2, bayesplot, mcclust, cluster, truncnorm
Suggests: spelling
Published: 2025-12-19
DOI: 10.32614/CRAN.package.DPCD (may not be active yet)
Author: Sam Morrissette [cph, aut, cre]
Maintainer: Sam Morrissette <samuel.morrissette01 at gmail.com>
BugReports: https://github.com/SamMorrissette/DPCD/issues
License: MIT + file LICENSE
URL: https://github.com/SamMorrissette/DPCD
NeedsCompilation: no
Language: en-US
Materials: README, NEWS
CRAN checks: DPCD results

Documentation:

Reference manual: DPCD.html , DPCD.pdf

Downloads:

Package source: DPCD_0.0.1.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): DPCD_0.0.1.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.