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>.

Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

Two implementations of canonical correlation analysis
        (CCA) that are based on iterated regression. By choosing the
        appropriate regression algorithm for each data domain, it is
        possible to enforce sparsity, non-negativity or other kinds of
        constraints on the projection vectors. Multiple canonical
        variables are computed sequentially using a generalized
        deflation scheme, where the additional correlation not
        explained by previous variables is maximized. nscancor() is
        used to analyze paired data from two domains, and has the same
        interface as cancor() from the 'stats' package (plus some extra
        parameters). mcancor() is appropriate for analyzing data from
        three or more domains. See
        <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
        and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
        details.