Package: soilcnn
Title: Convolutional Neural Networks for Digital Soil Mapping
Version: 0.1.0
Authors@R: c(
    person(c("Cássio", "Marques"), "Moquedace", , "cassiomoquedace@gmail.com",
           role = c("aut", "cre", "cph")),
    person(c("Clara", "Glória", "Oliveira"), "Baldi", role = c("aut", "cph")))
Description: A framework, built on 'torch', for mapping a soil property
        from point observations and a stack of aligned rasters with a
        convolutional network that reads the neighbourhood of each
        point. It extracts the patches; resamples by spatial blocks or
        by k-fold nearest neighbour distance matching, after
        Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>; tunes
        and selects under a noise floor and the one-standard-error
        rule; refits the selected configuration under several seeds;
        and predicts median, mean and interval maps over rasters of any
        extent. The map's centre is the ensemble median over seeds; the
        mean corrects its back-transformation by smearing, after Duan
        (1983) <doi:10.1080/01621459.1983.10478017>. The intervals are
        conformal, calibrated three ways and each checked on the test
        set: on the cross-validated residuals; on a calibration set
        held out of every fold, as split conformal, after Lei et al.
        (2018) <doi:10.1080/01621459.2017.1307116>; or with every
        fold's model at each pixel, as CV+, after Barber et al. (2021)
        <doi:10.1214/20-AOS1965> -- with points, or whole groups of
        them, weighing alike, after Dunn et al. (2023)
        <doi:10.1080/01621459.2022.2060112>. The area of applicability
        of Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650> is
        mapped beside them. What a model learned is read by
        permutation, by SHAP values from expected gradients, after
        Erion et al. (2021) <doi:10.1038/s42256-021-00343-w>, by SAGE,
        after Covert et al. (2020) <doi:10.48550/arXiv.2004.00668>, by
        accumulated local effects, after Apley and Zhu (2020)
        <doi:10.1111/rssb.12377>, and by refits without each variable.
        Random forest and multilayer perceptron baselines run under the
        same folds.
License: MIT + file LICENSE
URL: https://github.com/moquedace/soilcnn,
        https://moquedace.github.io/soilcnn/
BugReports: https://github.com/moquedace/soilcnn/issues
Depends: R (>= 4.5.0)
Imports: callr, coro, dplyr, graphics, grDevices, janitor, matrixStats,
        parallel, ps, purrr, readr, stats, terra, tibble, tools, torch,
        utils
Suggests: caret, CAST, FNN, knitr, pkgload, randomForest, ranger,
        rmarkdown, sf, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-GB
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-10-21 12:14:30 UTC; root
Author: Cássio Marques Moquedace [aut, cre, cph], Clara Glória Oliveira
        Baldi [aut, cph]
Maintainer: Cássio Marques Moquedace <cassiomoquedace@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-21 10:19:38 UTC
RemoteUrl: https://github.com/cran/soilcnn
RemoteRef: HEAD
RemoteSha: 2379c381d2cc1dea08270023c35fab0d5565455e