rebmix: Finite Mixture Modeling, Clustering & Classification
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Version: |
2.14.2 |
Depends: |
R (≥ 2.10.0) |
Imports: |
methods, stats, utils, graphics, grDevices |
Published: |
2022-08-17 |
Author: |
Marko Nagode
[aut, cre],
Branislav Panic
[ctb],
Jernej Klemenc
[ctb],
Simon Oman [ctb] |
Maintainer: |
Marko Nagode <marko.nagode at fs.uni-lj.si> |
License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: |
yes |
Citation: |
rebmix citation info |
Materials: |
NEWS |
In views: |
Cluster |
CRAN checks: |
rebmix results |
Documentation:
Downloads:
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