dpGMM
1.0.0Dynamic Programming Based Gaussian Mixture Modelling Tool for 1D and 2D Data
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 56%
- Documented parameters
- 100%
- Return-value docs
- 89%
- References docs
- 0%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest
- 0.2.22026-01-15
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-01-15
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 24 KB / 3 files
- Download size
- 569 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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