dpmixsim
0.0-9Dirichlet Process Mixture Model Simulation for Clustering and Image Segmentation
Overview
The 'dpmixsim' package implements a Dirichlet Process Mixture (DPM) model for clustering and image segmentation. The DPM model is a Bayesian nonparametric methodology that relies on MCMC simulations for exploring mixture models with an unknown number of components. The code implements conjugate models with normal structure (conjugate normal-normal DP mixture model). The package's applications are oriented towards the classification of magnetic resonance images according to tissue type or region of interest.
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Documentation
- Examples that run
- 10%
- Documented parameters
- 100%
- Return-value docs
- 67%
- References docs
- 75%
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Code & Tests
Datasets
People & History
6 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2019-11-11the request of the maintainer Had C++11 deprecation warnings
- RR 3.6.0 released · 2019-04-26
- 0.0-92018-07-11 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- RR 3.1.0 released · 2014-04-10
- RR 3.0.0 released · 2013-04-03
- 0.0-82012-07-24 · diff ↗
- RR 2.15.0 released · 2012-03-30
- RR 2.14.0 released · 2011-10-31
- 0.0-72011-06-20 · diff ↗
- 0.0-62011-06-14 · diff ↗
- RR 2.13.0 released · 2011-04-13
Show 4 earlier events
- RR 2.12.0 released · 2010-10-15
- 0.0-52010-09-06 · diff ↗
- 0.0-32010-04-29
- RR 2.11.0 released · 2010-04-22
Package metadata
- Total releases
- 6
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10.0
- Bundled data
- 0.6 KB / 1 file
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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