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: 2026-10-09 12:06:31 UTC; Computador
Author: Cássio Marques Moquedace [aut, cre, cph],
  Clara Glória Oliveira Baldi [aut, cph]
Maintainer: Cássio Marques Moquedace <cassiomoquedace@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-21 10:00:02 UTC
