hmmhdd
1.0Hidden Markov Models for High Dimensional Data
Overview
Some algorithms for the study of Hidden Markov Models for two different types of data. For the study of univariate and multivariate data in a finite framework, we provide some methods based on the definition of a Gaussian copula function to define the dependence between data (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2018) https://mox.polimi.it/publication-results/?id=776&tipo=add_qmox). For the study of functional data, we define an objective function based on distances between random curves to define the emission functions of the HMM (for further details, see Martino A., Guatteri, G. and Paganoni A. M. (2019) https://mox.polimi.it/publication-results/?id=805&tipo=add_qmox).
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Documentation
- Examples that run
- 73%
- Documented parameters
- 95%
- Return-value docs
- 100%
- References docs
- 81%
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Code & Tests
- Cyclomatic complexity
- 9.0 median / 26 max
Test coverage
Line coverage
–
Expression
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Tests / Examples
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Functions
13 9 exported
Complexity
9.5 avg / 26 max
Call network
13 nodes / 6 edges
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People & History
1 release. R releases are shown for context.
- RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2020-03-07depends on archived package 'roahd'
- 1.02019-09-04
- RR 3.6.0 released · 2019-04-26
Package metadata
- Total releases
- 1
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.6.0
- Bundled data
- 3.0 MB / 4 files
- Download size
- not tracked yet
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- With dependencies
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