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: 2026-09-13 10:09:33 UTC; Admin
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>
Repository: CRAN
Date/Publication: 2026-09-24 13:30:02 UTC
