Dirichlet Process Mixture Model Simulation for Clustering and Image Segmentation

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.


Changes in dpmixsim version 0.0-9

o Updated to remove the C keyword 'register'

Changes in dpmixsim version 0.0-8

o Correction for non-ASCII demo file

o Correction for console output

Changes in dpmixsim version 0.0-7

Corrections for partial argument match check-notes

Changes in dpmixsim version 0.0-6

o Updated data directories for compatibility with R version 2.13.0

Changes in dpmixsim version 0.0-5

o	Initialisation of the simulation may be performed with any number 
	of clusters between 1 and n (vector data dimension).

o	Simulation now estimates one variance per cluster.

o	Simulation runtime have been reduced by more than 40%, based on code

o Argument "minvar" has been added to dpmixsim() to control the minimum admissible cluster variance estimate.

o	Reversed the order of the steps in "src/gibbsclustersamplealpha.cc".
	Re-sampling steps are now performed before cluster management.

o	Introduced "testMarronWand" demo to test dpmixsim discriminating power.

o postdpmixciz() has been modified to go with the changes.

o	Bug fixed: sampling theta in birth of "newcluster".

First version released on CRAN: 0.0-3

Reference manual

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0.0-9 by Adelino Ferreira da Silva, 2 years ago

Browse source code at https://github.com/cran/dpmixsim

Authors: Adelino Ferreira da Silva <[email protected]>

Documentation:   PDF Manual  

Task views:

GPL (>= 2) license

Depends on oro.nifti, cluster

See at CRAN