README

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

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

Citation