SLGP-package: SLGP: A package for spatially dependent probability...
In SLGP: Spatial Logistic Gaussian Process for Field Density Estimation
SLGP-package R Documentation
SLGP: A package for spatially dependent probability distributions
Description
The SLGP package implements Spatial Logistic Gaussian Processes (SLGP) for the flexible modeling
of conditional and spatially dependent probability distributions. The SLGP framework leverages
basis-function expansions and sample-based inference (e.g., MAP, Laplace, MCMC) for efficient
density estimation and uncertainty quantification. This package includes functionality to define,
train, and sample from SLGP models, as well as visualization and diagnostic tools.
SLGP functions
The core functions in the package include:
-
slgp: trains an SLGP model from formula, data, and hyperparameters.
-
predictSLGP_moments: computes posterior predictive means and variances.
-
predictSLGP_quantiles: computes posterior predictive quantiles.
-
sampleSLGP: draws samples from the posterior predictive SLGP.
-
retrainSLGP: retrains a fitted SLGP object with new parameters or method.
Author(s)
Maintainer: Athénaïs Gautier athenais.gautier@onera.fr
References
Gautier, Athénaïs (2023). "Modelling and Predicting Distribution-Valued Fields with Applications to Inversion Under Uncertainty." Thesis, Universität Bern, Bern.
See the thesis online at https://boristheses.unibe.ch/4377/
SLGP documentation built on Sept. 9, 2025, 5:25 p.m.
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.
| SLGP-package | R Documentation |
SLGP: A package for spatially dependent probability distributions
Description
The SLGP package implements Spatial Logistic Gaussian Processes (SLGP) for the flexible modeling
of conditional and spatially dependent probability distributions. The SLGP framework leverages
basis-function expansions and sample-based inference (e.g., MAP, Laplace, MCMC) for efficient
density estimation and uncertainty quantification. This package includes functionality to define,
train, and sample from SLGP models, as well as visualization and diagnostic tools.
SLGP functions
The core functions in the package include:
-
slgp: trains an SLGP model from formula, data, and hyperparameters. -
predictSLGP_moments: computes posterior predictive means and variances. -
predictSLGP_quantiles: computes posterior predictive quantiles. -
sampleSLGP: draws samples from the posterior predictive SLGP. -
retrainSLGP: retrains a fitted SLGP object with new parameters or method.
Author(s)
Maintainer: Athénaïs Gautier athenais.gautier@onera.fr
References
Gautier, Athénaïs (2023). "Modelling and Predicting Distribution-Valued Fields with Applications to Inversion Under Uncertainty." Thesis, Universität Bern, Bern. See the thesis online at https://boristheses.unibe.ch/4377/
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.