Statistical and Machine Learning Engine for Long-Term Natural Resource Management Data // 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.