bpgmm
Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models
Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995).
- Version1.0.9
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- Last release06/01/2022
Team
Yaoxiang Li
MaintainerShow author details
Insights
Last 30 days
This package has been downloaded 209 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,611 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 33 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports9 packages
- Suggests1 package
- Linking To2 packages