GRaF-package: Species distribution modelling using latent Gaussian...
In goldingn/GRaF: Species Distribution Modelling using Gaussian Processes
Description
Details
Author(s)
References
See Also
Description
GRaF fits Gaussian process species distribution models using Laplace approximations.
Details
Package: GRaF
Type: Package
Version: 0.1-13
Date: 2014-03-07
License: GPL (>= 2)
GRaF provides functions to fit, visualise, and predict from Gaussian process models for binary data, with an emphasis on species distribution models. The principle function is graf which returns a graf object, which can be manipulated using plot, print and predict methods among others.
GRaF is under active development and future releases will enable a range of additional methods, which may (or may not) include: models for count data, spatially and temporally explicit models, the ability to limit interactions between covariates, multi-species models and input and output of raster imagery. Please feel free to contact the maintainer with bug reports and suggestions!
The development version of this package is hosted on github (https://github.com/goldingn/GRaF) and a brief tutorial is available here: https://rawgithub.com/goldingn/intecol2013/master/tutorial/graf_workshop.html
Author(s)
Nick Golding <nick.golding@linacre.ox.ac.uk>
References
Golding & Purse (2013) GRaF: Fast and Flexible Bayesian Species Distribution Modelling Using Gaussian
Random Fields. Manuscript in preparation. (preprint as Chapter 5 in http://dx.doi.org/10.6084/m9.figshare.767289)
See Also
goldingn/GRaF documentation built on May 17, 2019, 7:41 a.m.
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Description Details Author(s) References See Also
Description
GRaF fits Gaussian process species distribution models using Laplace approximations.
Details
| Package: | GRaF |
| Type: | Package |
| Version: | 0.1-13 |
| Date: | 2014-03-07 |
| License: | GPL (>= 2) |
GRaF provides functions to fit, visualise, and predict from Gaussian process models for binary data, with an emphasis on species distribution models. The principle function is graf which returns a graf object, which can be manipulated using plot, print and predict methods among others.
GRaF is under active development and future releases will enable a range of additional methods, which may (or may not) include: models for count data, spatially and temporally explicit models, the ability to limit interactions between covariates, multi-species models and input and output of raster imagery. Please feel free to contact the maintainer with bug reports and suggestions!
The development version of this package is hosted on github (https://github.com/goldingn/GRaF) and a brief tutorial is available here: https://rawgithub.com/goldingn/intecol2013/master/tutorial/graf_workshop.html
Author(s)
Nick Golding <nick.golding@linacre.ox.ac.uk>
References
Golding & Purse (2013) GRaF: Fast and Flexible Bayesian Species Distribution Modelling Using Gaussian Random Fields. Manuscript in preparation. (preprint as Chapter 5 in http://dx.doi.org/10.6084/m9.figshare.767289)
See Also
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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