Package: xaiHydro
Type: Package
Title: Explainable AI Tools for Hydro-Climate Modelling
Version: 0.1.0
Date: 2026-09-09
Authors@R: c(
    person("Sadikul", "Islam",
           email   = "sadikul.islamiasri@gmail.com",
           role    = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2924-7122")),
    person("Shakir",  "Ali",
           role    = "aut"),
    person("Rajesh",  "Kaushal",
           role    = "aut"))
Description: Provides a unified workflow for applying Explainable
        Artificial Intelligence (XAI) methods to hydro-climate
        predictive models. Functions implement a permutation-based
        Monte Carlo SHAP estimator (Strumbelj and Kononenko (2014)
        <doi:10.1007/s10115-013-0679-x>; Lundberg and Lee (2017)
        <doi:10.48550/arXiv.1705.07874>), a self-contained locally
        weighted linear surrogate LIME (Ribeiro et al. (2016)
        <doi:10.1145/2939672.2939778>), and Partial Dependence Plots
        with Accumulated Local Effects (Friedman (2001)
        <doi:10.1214/aos/1013203451>; Apley and Zhu (2020)
        <doi:10.1111/rssb.12377>) with hydrology-specific
        visualisations and interpretation utilities. Supports any model
        object compatible with the 'DALEX' explainer interface (Biecek
        (2018) <doi:10.18637/jss.v097.i01>), including random forests,
        gradient boosting, and neural networks trained on streamflow,
        drought indices, flood risk, or evapotranspiration data.
        Hydrology-standard performance metrics Nash-Sutcliffe
        Efficiency (NSE, Nash and Sutcliffe (1970)
        <doi:10.1016/0022-1694(70)90255-6>) and Kling-Gupta Efficiency
        (KGE, Gupta et al. (2009) <doi:10.1016/j.jhydrol.2009.08.003>)
        are computed alongside standard regression metrics. Designed to
        accompany the book chapter: Islam, S., Dheeraj, A., Ali, S.,
        Kaushal, R. and Venkatesh, G. (2026). Explainable Artificial
        Intelligence for Hydro-Climatic Modelling: Methods,
        Applications, and Implementation Using the xaiHydro R Package.
        In Chandniha, S. K. et al. (Eds.), Hydro-Climate Analytics:
        Remote Sensing, AI and Geospatial Modelling. Springer.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Depends: R (>= 4.1.0)
Imports: DALEX (>= 2.4.0), ggplot2 (>= 3.4.0), dplyr (>= 1.1.0), tidyr
        (>= 1.3.0), patchwork (>= 1.2.0), cli (>= 3.6.0), rlang (>=
        1.1.0)
Suggests: randomForest (>= 4.7.0), xgboost (>= 1.7.0), ranger (>=
        0.16.0), testthat (>= 3.0.0), knitr (>= 1.45), rmarkdown (>=
        2.25)
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
Packaged: 2026-09-25 17:46:19 UTC; root
Author: Sadikul Islam [aut, cre] (ORCID:
        <https://orcid.org/0000-0003-2924-7122>), Shakir Ali [aut],
        Rajesh Kaushal [aut]
Maintainer: Sadikul Islam <sadikul.islamiasri@gmail.com>
Config/pak/sysreqs: libicu-dev
Repository: https://sadikulislamiasri-hub.r-universe.dev
Date/Publication: 2026-09-24 15:20:07 UTC
RemoteUrl: https://github.com/cran/xaiHydro
RemoteRef: HEAD
RemoteSha: 6241613e6f18a04212ccf3ede08024d3dae5cd72