GPEDM-package: Gaussian Process regression for Empirical Dynamic Modeling
In tanyalrogers/GPEDM: Gaussian Process regression for Empirical Dynamic Modeling
GPEDM-package R Documentation
Gaussian Process regression for Empirical Dynamic Modeling
Description
Functions for fiting hierarchical, separable length scale GP models with
automatic relevance determination (ARD) for use in Empirical Dynamic Modeling (EDM)
and other applications.
Details
The main function is fitGP which is used to train the model and can also
produce predictions if desired. Use predict.GP to generate other or additional
predictions from a fitted model, plot.GPpred to plot observed and predicted
values and getconditionals to obtain condictional reponses. Also available
for use is the function makelags which can be used to create delay vectors.
See the (yet to be created) vignette for more detailed instructions.
Author(s)
Original MATLAB code by Stephan Munch, adapted to R by Tanya Rogers.
Maintainer: Tanya Rogers <tanya.rogers@noaa.gov>
We thank Bethany Johnson, Lucas Medeiros, and Vadim Karatayev for beta testing and
improvement suggestions.
References
Munch, S. B., Poynor, V., and Arriaza, J. L. 2017. Circumventing structural uncertainty:
a Bayesian perspective on nonlinear forecasting for ecology. Ecological Complexity, 32:134.
Johnson, B., and Munch, S. B. 2022. An empirical dynamic modeling framework for missing or
irregular samples. Ecological Modelling, 468:109948.
See Also
fitGP, plot.GPpred, getconditionals,
makelags
tanyalrogers/GPEDM documentation built on June 1, 2025, 8:10 p.m.
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| GPEDM-package | R Documentation |
Gaussian Process regression for Empirical Dynamic Modeling
Description
Functions for fiting hierarchical, separable length scale GP models with automatic relevance determination (ARD) for use in Empirical Dynamic Modeling (EDM) and other applications.
Details
The main function is fitGP which is used to train the model and can also
produce predictions if desired. Use predict.GP to generate other or additional
predictions from a fitted model, plot.GPpred to plot observed and predicted
values and getconditionals to obtain condictional reponses. Also available
for use is the function makelags which can be used to create delay vectors.
See the (yet to be created) vignette for more detailed instructions.
Author(s)
Original MATLAB code by Stephan Munch, adapted to R by Tanya Rogers.
Maintainer: Tanya Rogers <tanya.rogers@noaa.gov>
We thank Bethany Johnson, Lucas Medeiros, and Vadim Karatayev for beta testing and improvement suggestions.
References
Munch, S. B., Poynor, V., and Arriaza, J. L. 2017. Circumventing structural uncertainty: a Bayesian perspective on nonlinear forecasting for ecology. Ecological Complexity, 32:134.
Johnson, B., and Munch, S. B. 2022. An empirical dynamic modeling framework for missing or irregular samples. Ecological Modelling, 468:109948.
See Also
fitGP, plot.GPpred, getconditionals,
makelags
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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