SLGP: An R Package for Spatial Conditional Density Estimation
Abstract
The SLGP package provides nonparametric estimation of fields of probability densities indexed by covariates, from unevenly sized samples with few or no replicates. A Spatial Logistic Gaussian Process applies a logistic density transformation to a finite-rank Gaussian process over a joint index-response domain, yielding a prior over conditional densities that adapts in location, shape, and modality. The contribution of this paper is the implementation: a finite-rank Random-Fourier-Feature representation that keeps the transformed process numerically tame, a log-concave likelihood that makes the fit a convex problem, and a family of quadrature shortcuts for the normalising integrals that turn a per-observation cost into a per-grid cost. Three estimation modes are available: MAP, Laplace, and full MCMC. We demonstrate the package throughout on the classical Fiji-Tonga earthquake catalogue, estimating how the distribution of hypocentre depths deforms across the region, and on a discrete-response variant.
Related
- LUCID: Local Understanding of Covariate Importance with Conditional Density Estimation
- Minimax estimation of the expected conditional covariance under bounds on the covariate density
- Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression
- Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
- Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach
BibTeX
@misc{gautier2026slgp,
title = {{SLGP: An R Package for Spatial Conditional Density Estimation}},
author = {Athénaïs Gautier},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.33827},
eprint = {2609.33827},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.33827},
}
RIS
TY - UNPB
TI - SLGP: An R Package for Spatial Conditional Density Estimation
AU - Athénaïs Gautier
PY - 2026
DA - 2026/09/27
PB - arXiv
DO - 10.48550/arxiv.2609.33827
UR - https://arxiv.org/abs/2609.33827
AB - The SLGP package provides nonparametric estimation of fields of probability densities indexed by covariates, from unevenly sized samples with few or no replicates. A Spatial Logistic Gaussian Process applies a logistic density transformation to a finite-rank Gaussian process over a joint index-response domain, yielding a prior over conditional densities that adapts in location, shape, and modality. The contribution of this paper is the implementation: a finite-rank Random-Fourier-Feature representation that keeps the transformed process numerically tame, a log-concave likelihood that makes the fit a convex problem, and a family of quadrature shortcuts for the normalising integrals that turn a per-observation cost into a per-grid cost. Three estimation modes are available: MAP, Laplace, and full MCMC. We demonstrate the package throughout on the classical Fiji-Tonga earthquake catalogue, estimating how the distribution of hypocentre depths deforms across the region, and on a discrete-response variant.
ER -