MLSampling/DESCRIPTION at master · ccarbajal16/MLSampling · GitHub

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Package: MLSampling
Type: Package
Title: Machine Learning-Based Spatial Sampling Optimization Framework
Version: 0.0.1
Date: 2025-09-22
Author: Carlos Carbajal
Maintainer: Carlos <ccarbajal@educagis.com>
Description: Comprehensive machine learning-based spatial sampling optimization framework integrating
    Bayesian Deep Learning (BDL), Random Forest optimization, Unified Deep Learning (UDL), and
    Unified Feature Network (UFN) models. Provides advanced ML techniques for uncertainty quantification,
    feature importance analysis, and systematic sampling design comparison with comprehensive performance
    metrics and reporting capabilities. Follows constitutional principles for R spatial analysis excellence
    with modern terra/sf packages.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: false
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Depends:
    R (>= 4.3.0)
Imports:
    terra (>= 1.7.0),
    sf (>= 1.0.0),
    torch (>= 0.11.0),
    R6 (>= 2.5.0),
    dplyr,
    ggplot2,
    plotly,
    leaflet,
    DT,
    shiny,
    shinydashboard,
    igraph,
    GA,
    GenSA,
    caret,
    randomForest,
    e1071,
    corrplot,
    viridis,
    RColorBrewer,
    gridExtra,
    knitr,
    rmarkdown,
    MASS,
    microbenchmark,
    progress
Suggests:
    testthat (>= 3.1.0),
    quickcheck (>= 0.1.0),
    roxygen2 (>= 7.2.0),
    covr (>= 3.6.0),
    lintr (>= 3.0.0),
    pkgdown (>= 2.0.0),
    future (>= 1.28.0),
    stars (>= 0.6.0),
    pryr
Remotes:
    hadley/pryr
VignetteBuilder: knitr
Config/Needs/website: pkgdown
URL: https://ccarbajal16.github.io/MLSampling/, https://github.com/ccarbajal16/MLSampling
BugReports: https://github.com/ccarbajal16/MLSampling/issues