NRMstatsML

NRMstatsML is a comprehensive R package providing a statistical and machine learning engine for long-term Natural Resource Management (NRM) datasets. It integrates formula-driven approaches, statistical inference, and machine learning for reproducible analytics across soil, water, crop, and climate domains.

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Installation

# Install from CRAN
install.packages("NRMstatsML")

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Core Modules

Module	Key Functions	Description
trendML	nrm_trend(), nrm_mann_kendall(), nrm_sens_slope(), nrm_structural_break()	Monotonic trend tests, slope estimation, structural break detection
multiSysML	nrm_multivariate(), nrm_pls(), nrm_sem()	OLS, PLS, Structural Equation Modelling
responseML	nrm_response_curve(), nrm_optimize_input()	Yield-response curves and input optimisation
tsML	nrm_forecast(), nrm_arima()	ARIMA/SARIMA forecasting with prediction intervals
panelML	nrm_panel(), nrm_did()	Fixed/random effects, difference-in-differences
uncertaintyML	nrm_uncertainty(), nrm_bootstrap(), nrm_monte_carlo()	Bootstrap, Monte Carlo, Bayesian uncertainty
autoML	nrm_automl(), nrm_benchmark()	Automated model selection and benchmarking

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Quick Start

library(NRMstatsML)

# Load synthetic example data
data(nrm_example)

# 1. Validate data
nrm_data_check(nrm_example)

# 2. Trend analysis
trend <- nrm_trend(nrm_example, time_var = "year", value_var = "crop_yield")
print(trend)
nrm_plot(trend)

# 3. Yield-response curve
rc  <- nrm_response_curve(nrm_example, input_var = "N",
                           response_var = "crop_yield", type = "quadratic")
opt <- nrm_optimize_input(rc, price_ratio = 0.6)
print(opt)

# 4. Forecast next 5 years
fc <- nrm_forecast(nrm_example, value_var = "crop_yield", horizon = 5)
nrm_plot(fc)

# 5. Bootstrap uncertainty of mean yield
bs <- nrm_bootstrap(nrm_example,
                    stat_fn = function(d) mean(d$crop_yield),
                    n_iter  = 1000)
print(bs)

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Design Principles

• Consistent API: every module follows nrm_<verb>() naming.
• CRAN compliant: no global state, explicit imports, documented datasets.
• Reproducible: set seed arguments for deterministic results.
• Modular: use individual module functions or the high-level wrappers.
• ggplot2-based visualisation: all nrm_plot() methods return editable ggplot objects.

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Citation

citation("NRMstatsML")

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License

GPL (>= 3). See the GNU General Public License for details.