Package: spboost
Type: Package
Title: Gradient Boosting for Nonlinear Spatial Autoregressive Models
Version: 0.7.0
Authors@R: person(given = "Ghislain",
                  family = "Geniaux",
                  role = c("aut", "cre"),
                  email = "ghislain.geniaux@inrae.fr")
Description: Flexible nonlinear extension of spatial autoregressive (SAR),
    spatial error (SEM), and spatial autoregressive with autoregressive
    disturbances (SARAR) models with multiple regression engines
    (generalized additive models ('mgcv'), gradient boosting ('mboost'),
    multivariate adaptive regression splines ('earth'), and 'xgboost')
    and two families of spatial-parameter estimators: maximum likelihood
    and the determinant-free Closed-Form Estimator of Smirnov (2020)
    <doi:10.1111/gean.12268>. See Geniaux G. (2026). "Flexible nonlinear
    spatial autoregressive models: a gradient boosting approach with
    closed-form estimation." Presented at Spatial Econometrics World
    Congress (SEA/SEW 2026, Paris), unpublished.
License: GPL (>= 2)
Depends: Matrix, mboost, mgcv, methods, mgwrsar
Imports: Rcpp, sf, MASS, data.table, xgboost, caret, doParallel,
        foreach, nabor, earth
Suggests: blockCV, knitr, rmarkdown, RSpectra, spatialreg, spdep,
        testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
LinkingTo: RcppEigen, Rcpp
RoxygenNote: 7.3.2
NeedsCompilation: yes
Encoding: UTF-8
Packaged: 2026-06-02 13:54:02 UTC; geniaux
Author: Ghislain Geniaux [aut, cre]
Maintainer: Ghislain Geniaux <ghislain.geniaux@inrae.fr>
Repository: CRAN
Date/Publication: 2026-06-08 18:00:02 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-06-22 12:28:00 UTC; windows
Archs: x64
