dpGMM: Dynamic Programming Based Gaussian Mixture Modelling Tool for 1D
and 2D Data
Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting. For more details see Zyla, J., Szumala, K., Polanski, A., Polanska, J., & Marczyk, M. (2026) <doi:10.1016/j.jocs.2026.102811>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.5), ggplot2, RColorBrewer, stats, pracma |
| Imports: |
grDevices, ggpubr, Matrix, reshape2, graphics, methods, mvtnorm |
| Published: |
2026-03-02 |
| DOI: |
10.32614/CRAN.package.dpGMM |
| Author: |
Michal Marczyk [aut, ctb],
Kamila Szumala [aut, cre],
Joanna Zyla [aut, ctb] |
| Maintainer: |
Kamila Szumala <kamila.szumala at polsl.pl> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
dpGMM results |
Documentation:
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