Statistical and Machine Learning Engine for Long-Term Natural Resource Management Data R package NRMstatsML version 0.1.4 A comprehensive toolkit for statistical and machine learning-based analysis of long-term Natural Resource Management (NRM) datasets. Integrates formula-driven approaches, statistical inference, and machine learning (ML) models for advanced analytics. Modules cover trend and structural analysis (Mann-Kendall test, slope estimation, Chow test, structural break detection), multivariate system modelling (Partial Least Squares (PLS), Structural Equation Modelling (SEM)), response curve optimisation, time-series forecasting (Autoregressive Integrated Moving Average (ARIMA), hybrid models), panel data and treatment effects (Difference-in-Differences (DiD), causal machine learning), uncertainty and sensitivity analysis (bootstrap, Monte Carlo, Bayesian), and automated model selection and performance comparison. Designed for long-term datasets covering soil, water, crop, and climate domains. Key references: Mann and Kendall (1945) <doi:10.2307/1907187>; Sen (1968) <doi:10.1080/01621459.1968.10480934>; Bai and Perron (2003) <doi:10.1002/jae.659>; Rosseel (2012) <doi:10.18637/jss.v048.i02>; Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. Software Depends: R (>= 4.1.0) Imports: Kendall (>= 2.2), trend (>= 1.1.4), strucchange (>= 1.5.3), plm (>= 2.6.0), forecast (>= 8.20), lavaan (>= 0.6.12), pls (>= 2.8.0), caret (>= 6.0.93), boot (>= 1.3.28), ggplot2 (>= 3.4.0), rlang (>= 1.1.0), stats, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown, keras, tensorflow, BayesianTools, sensitivity, mboost, mlr3, covr Sadikul Islam <sadikul.islamiasri@gmail.com> Comprehensive R Archive Network (CRAN) Sadikul Islam [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-2924-7122>) GPL (>= 3) 2026-06-07 application/tgz https://CRAN.R-project.org/package=NRMstatsML doi:10.32614/CRAN.package.NRMstatsML en-US