DESCRIPTION

Package: spatialRF
Title: Easy Spatial Modeling with Random Forest
Version: 1.0.6
Authors@R: 
    person(given = "Blas M.",
           family = "Benito",
           role = c("aut", "cre", "cph"),
           email = "blasbenito@gmail.com",
           comment = c(ORCID = "0000-0001-5105-7232"))
URL: https://github.com/BlasBenito/spatialRF
BugReports: https://github.com/BlasBenito/spatialRF/issues
Description: Spatial regression modeling on regular or irregular point data with random forest via generation of spatial predictors from a distance matrix using three different methods: 'Moran's Eigenvector Maps' and 'PCA factors of the distance matrix' (Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>), and 'distance matrix columns as explanatory variables' (Hengl et al. <DOI:10.7717/peerj.5518>). The spatial predictors allow the model to take into account the spatial structure of the training data and minimize spatial autocorrelation in the model residuals, and provide context to better assess the importance of non-spatial predictors when compared to the importance of the spatial ones. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>), and are designed to run in parallel in a single machine or a Beowulf cluster. The package provides as well tools to reduce multicollinearity, identify important variable interactions, tune random forest hyperparameters, assess model performance on spatially independent data folds, and examine the resulting models via importance plots and response curves and surfaces. 
License: GPL-3
Depends: 
    R (>= 2.10)
Imports: 
    dplyr,
    ggplot2,
    magrittr,
    stats,
    tibble,
    utils,
    foreach,
    doParallel,
    ranger,
    rlang,
    tidyr,
    tidyselect,
    huxtable,
    patchwork,
    viridis
Suggests: 
    testthat,
    spelling
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, old_usage = FALSE)
RoxygenNote: 7.1.1
Language: en-US