Package: SLGP
Type: Package
Title: Spatial Logistic Gaussian Process for Field Density Estimation
Version: 1.0.2
Authors@R: 
    c(person(given = "Athénaïs",
             family = "Gautier",
             role = c("aut", "cre"),
             email = "athenais.gautier@onera.fr"))
Maintainer: Athénaïs Gautier <athenais.gautier@onera.fr>
Description: Provides tools for conditional and spatially dependent 
    density estimation using Spatial Logistic Gaussian Processes (SLGPs). 
    The approach represents probability densities through finite-rank 
    Gaussian process priors transformed via a spatial logistic density 
    transformation, enabling flexible non-parametric modeling of 
    heterogeneous data. Functionality includes density prediction, 
    quantile and moment estimation, sampling methods, and preprocessing 
    routines for basis functions. Applications arise in spatial statistics, 
    machine learning, and uncertainty quantification. 
    The methodology builds on the framework of Leonard (1978) 
    <doi:10.1111/j.2517-6161.1978.tb01655.x>, Lenk (1988) <doi:10.1080/01621459.1988.10478625>, 
    Tokdar (2007) <doi:10.1198/106186007X210206>, Tokdar (2010) <doi:10.1214/10-BA605>, 
    and is further aligned with recent developments 
    in Bayesian non-parametric modelling: see Gautier (2023) <https://boristheses.unibe.ch/4377/>, 
    and Gautier (2025) <doi:10.48550/arXiv.2110.02876>).
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.3.2
Biarch: true
Depends: R (>= 3.5.0), stats
Imports: DiceDesign, methods, mvnfast, Rcpp (>= 0.12.0), RcppParallel
        (>= 5.0.1), rstan (>= 2.18.1), GoFKernel, rstantools
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
        rstan (>= 2.18.1), StanHeaders (>= 2.21.0)
SystemRequirements: GNU make
Suggests: knitr, rmarkdown, tidyr, dplyr, ggplot2, ggpubr, viridis,
        MASS
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-02-17 19:10:30 UTC; agautier
Author: Athénaïs Gautier [aut, cre]
Repository: CRAN
Date/Publication: 2026-02-17 21:00:02 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-04-29 19:38:08 UTC; windows
Archs: x64
