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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.

Installation

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

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

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)

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.

Citation

citation("NRMstatsML")

License

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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