Package {NRMstatsML}

Type:	Package
Title:	Statistical and Machine Learning Engine for Long-Term Natural Resource Management Data
Version:	0.1.4
Description:	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>.
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
Language:	en-US
Depends:	R (≥ 4.1.0)
Imports:	Kendall (≥ 2.2), trend (≥ 1.1.4), strucchange (≥ 1.5.3), plm (≥ 2.6.0), forecast (≥ 8.20), lavaan (≥ 0.6.12), pls (≥ 2.8.0), caret (≥ 6.0.93), boot (≥ 1.3.28), ggplot2 (≥ 3.4.0), rlang (≥ 1.1.0), stats, utils
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, keras, tensorflow, BayesianTools, sensitivity, mboost, mlr3, covr
VignetteBuilder:	knitr
RoxygenNote:	7.3.3
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-06-01 09:22:01 UTC; acer
Author:	Sadikul Islam [aut, cre, cph]
Maintainer:	Sadikul Islam <sadikul.islamiasri@gmail.com>
Repository:	CRAN
Date/Publication:	2026-06-07 18:30:08 UTC

NRMstatsML: Statistical and Machine Learning Engine for Long-Term NRM Data

Description

A comprehensive R package for statistical and machine learning-based analysis of long-term Natural Resource Management (NRM) datasets. It integrates formula-driven approaches, statistical inference, and machine learning models for advanced analytics across soil, water, crop, and climate domains.

Core Modules

trendML

Trend and structural analysis: Mann-Kendall test, Sen's slope, structural break detection (Chow, Bai-Perron), and ML-based trend prediction. See nrm_trend, nrm_mann_kendall.

multiSysML

Multivariate system modelling: regression, PLS, SEM, and ML feature selection. See nrm_multivariate, nrm_pls.

responseML

Response curve and input optimisation: quadratic and nonlinear regression. See nrm_response_curve, nrm_optimize_input.

tsML

Time series forecasting: ARIMA, SARIMA, LSTM, and hybrid models. See nrm_forecast, nrm_arima.

panelML

Panel data and treatment effects: fixed/random effects, DiD, causal ML. See nrm_panel, nrm_did.

uncertaintyML

Uncertainty and sensitivity analysis: bootstrap, Monte Carlo, Bayesian modelling. See nrm_uncertainty, nrm_bootstrap.

autoML

Automated model selection, tuning, and benchmarking. See nrm_automl, nrm_benchmark.

Typical Workflow

1. Validate and prepare data with nrm_data_check.

2. Run trend analysis with nrm_trend or nrm_mann_kendall.

3. Fit models with the relevant module function.

4. Summarise results with nrm_summary.

5. Visualise with nrm_plot.

Author(s)

Maintainer: Sadikul Islam sadikul.islamiasri@gmail.com (ORCID) [copyright holder]

References

Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259.

Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63, 1379–1389.

ARIMA model for NRM time series

Description

Fits an ARIMA model with automatic order selection via forecast.

Usage

nrm_arima(data, value_var, time_var = "year", seasonal = TRUE, frequency = 1L)

Arguments

Value

A list of class "nrm_arima" with the fitted model and AIC/BIC.

Examples

data(nrm_example)
ar <- nrm_arima(nrm_example, value_var = "crop_yield")
print(ar)

Automated model selection and tuning

Description

Trains multiple model types via caret, selects the best model by cross-validated Root Mean Square Error (RMSE), and returns a ranked comparison table.

Usage

nrm_automl(
  data,
  formula,
  methods = c("lm", "rf", "gbm", "svmRadial"),
  cv_folds = 5L,
  seed = 42L
)

Arguments

Value

A list of class "nrm_automl" with:

best_model

The best-performing trained caret model.

best_method

Name of the best method.

leaderboard

Data frame ranking all methods by Root Mean Square Error (RMSE).

models

Named list of all fitted models.

Examples

data(nrm_example)
am <- nrm_automl(nrm_example,
                 formula  = crop_yield ~ N + P + K + rainfall,
                 methods  = c("lm", "rf"),
                 cv_folds = 5)
nrm_summary(am)

Benchmark model metrics on a hold-out test set

Description

Evaluates a set of model objects against a hold-out test set and computes Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared.

Usage

nrm_benchmark(models, test_data, response_var)

Arguments

Value

A list of class "nrm_benchmark" containing a data frame metrics with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared, ranked by RMSE.

Examples

data(nrm_example)
n     <- nrow(nrm_example)
train <- nrm_example[seq_len(floor(0.8 * n)), ]
test  <- nrm_example[seq(floor(0.8 * n) + 1L, n), ]
m1    <- lm(crop_yield ~ N + P + K, data = train)
bm    <- nrm_benchmark(list(ols = m1), test_data = test,
                       response_var = "crop_yield")
print(bm)

Bootstrap uncertainty estimation

Description

Estimates confidence intervals for a user-defined statistic via non-parametric bootstrapping using boot.

Usage

nrm_bootstrap(data, stat_fn, n_iter = 1000L, alpha = 0.05, ...)

Arguments

Value

A list of class "nrm_bootstrap" with mean, sd, ci, and raw (the boot object).

Examples

data(nrm_example)
bs <- nrm_bootstrap(nrm_example,
        stat_fn = function(d) mean(d$crop_yield),
        n_iter = 500)
print(bs)

Validate and summarise an NRM dataset

Description

Checks a data frame for missing values, type consistency, and expected columns for NRM analysis. Returns a structured report.

Usage

nrm_data_check(data, time_var = "year", verbose = TRUE)

Arguments

Value

A list (invisibly) with components n_rows, n_cols, missing_summary, numeric_cols, and warnings.

Examples

data(nrm_example)
nrm_data_check(nrm_example)

Difference-in-Differences (DiD) estimator

Description

Estimates the average treatment effect on the treated (ATT) using a two-period, two-group Difference-in-Differences (DiD) design appropriate for policy or intervention evaluation in Natural Resource Management (NRM) contexts.

Usage

nrm_did(data, outcome, treat_var, time_var, covariates = NULL)

Arguments

Value

A list of class "nrm_did" with:

att

Average treatment effect on the treated (ATT).

model

The underlying lm object.

p_value

P-value of the DiD interaction term.

Examples

set.seed(42)
d <- data.frame(
  crop_yield = c(rnorm(20, 3.5, 0.4), rnorm(20, 4.2, 0.4)),
  treatment  = rep(c(0L, 1L), each = 20),
  post       = rep(c(0L, 1L, 0L, 1L), each = 10)
)
did <- nrm_did(d,
               outcome   = "crop_yield",
               treat_var = "treatment",
               time_var  = "post")
print(did)

Example long-term NRM dataset

Description

A synthetic dataset mimicking 20 years of a long-term fertiliser experiment covering crop yield, soil properties, nutrient inputs, and climate variables. Generated for illustration and testing purposes only.

Usage

nrm_example

Format

A data frame with 20 rows and 10 variables:

year

Integer. Year of observation (2000–2019).

treatment

Character. Treatment label ("control" or "NPK").

crop_yield

Numeric. Grain yield (t/ha).

soil_OC

Numeric. Soil organic carbon (%).

N

Numeric. Nitrogen applied (kg/ha).

P

Numeric. Phosphorus applied (kg/ha).

K

Numeric. Potassium applied (kg/ha).

rainfall

Numeric. Annual rainfall (mm).

runoff

Numeric. Annual runoff (mm).

biomass

Numeric. Total biomass (t/ha).

Source

Synthetically generated; not derived from any real experiment.

Examples

data(nrm_example)
head(nrm_example)
nrm_data_check(nrm_example)

Forecast NRM time series

Description

High-level wrapper that fits an ARIMA or hybrid ARIMA+ML model and returns a forecast with confidence intervals.

Usage

nrm_forecast(
  data,
  value_var,
  time_var = "year",
  horizon = 5L,
  method = "auto_arima",
  frequency = 1L
)

Arguments

Value

A list of class "nrm_forecast" with:

forecast

The forecast object.

method

Method used.

horizon

Forecast horizon.

accuracy

In-sample accuracy metrics.

Examples

data(nrm_example)
fc <- nrm_forecast(nrm_example, value_var = "crop_yield", horizon = 5)
nrm_plot(fc)

Mann-Kendall trend test

Description

Applies the Mann-Kendall nonparametric test for monotonic trend in a time series. Suitable for non-normally distributed and autocorrelated data typical in long-term NRM records.

Usage

nrm_mann_kendall(data, time_var = "year", value_var, alpha = 0.05)

Arguments

Value

A list of class "nrm_mann_kendall" with components:

tau

Kendall's tau statistic.

p_value

Two-sided p-value.

significant

Logical; TRUE when p < alpha.

trend_direction

Character: "increasing", "decreasing", or "no trend".

raw

Full output from MannKendall.

References

Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259.

Kendall, M. G. (1975). Rank Correlation Methods. Griffin.

Examples

data(nrm_example)
mk <- nrm_mann_kendall(nrm_example, time_var = "year",
                       value_var = "crop_yield")
print(mk)

Monte Carlo uncertainty simulation

Description

Generates Monte Carlo samples by adding Gaussian noise (scaled to the column-wise standard deviation) to each numeric column and evaluates the statistic of interest across simulations.

Usage

nrm_monte_carlo(
  data,
  stat_fn,
  n_iter = 1000L,
  alpha = 0.05,
  noise_sd = 0.1,
  ...
)

Arguments

Value

A list of class "nrm_monte_carlo" with mean, sd, ci, and samples.

Examples

data(nrm_example)
mc <- nrm_monte_carlo(nrm_example,
        stat_fn = function(d) mean(d$crop_yield),
        n_iter = 500)
print(mc)

Multivariate regression for Natural Resource Management systems

Description

Fits a multivariate Ordinary Least Squares (OLS) regression model and returns standardised diagnostics suitable for Natural Resource Management (NRM) data.

Usage

nrm_multivariate(data, formula, scale = TRUE)

Arguments

Value

A list of class "nrm_multivariate" with components:

model

The fitted lm object.

coefficients

Tidy coefficient table.

r_squared

Adjusted R-squared.

vif

Variance inflation factors (requires car).

Examples

data(nrm_example)
mv <- nrm_multivariate(nrm_example,
        formula = crop_yield ~ N + P + K + rainfall)
nrm_summary(mv)

Optimise input level for maximum response

Description

Given a fitted response curve, computes the economically optimal input level based on input cost and output price ratios.

Usage

nrm_optimize_input(curve_result, price_ratio = 1)

Arguments

Value

A list of class "nrm_optimize_input" with:

biophysical_optimum

Input giving maximum biological yield.

economic_optimum

Input giving maximum economic return.

price_ratio

The price ratio used.

Examples

data(nrm_example)
rc  <- nrm_response_curve(nrm_example, input_var = "N",
                           response_var = "crop_yield")
opt <- nrm_optimize_input(rc, price_ratio = 0.5)
print(opt)

Panel data regression for Natural Resource Management experiments

Description

Fits fixed-effects or random-effects panel models using plm, with a Hausman test to guide model selection between fixed and random effects.

Usage

nrm_panel(data, formula, index, model = "within")

Arguments

Value

A list of class "nrm_panel" with:

model

The fitted plm object.

hausman

Hausman test result (fixed effects vs random effects).

summary

Model summary.

References

Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of Statistical Software, 27(2), 1–43. doi:10.18637/jss.v027.i02

Examples

set.seed(1)
d <- data.frame(
  site       = rep(c("A", "B", "C", "D"), each = 10),
  year       = rep(2010:2019, times = 4),
  crop_yield = rnorm(40, 4, 0.5),
  N          = rnorm(40, 90, 10),
  rainfall   = rnorm(40, 650, 50)
)
pm <- nrm_panel(d,
        formula = crop_yield ~ N + rainfall,
        index   = c("site", "year"),
        model   = "within")
nrm_summary(pm)

Generic plot for NRMstatsML objects

Description

Produces a ggplot2 visualisation appropriate to the result type.

Usage

nrm_plot(x, ...)

Arguments

Value

A ggplot2 object (invisibly).

Partial Least Squares (PLS) regression

Description

Fits a Partial Least Squares (PLS) regression model using plsr, suitable when predictors are collinear or when the number of predictors approaches the number of observations.

Usage

nrm_pls(data, formula, ncomp = NULL, validation = "CV")

Arguments

Value

A list of class "nrm_pls" with components:

model

The fitted plsr object.

ncomp

Number of components used.

rmsep

Root Mean Square Error of Prediction (RMSEP) by component.

References

Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130.

Examples

data(nrm_example)
pl <- nrm_pls(nrm_example,
              formula = crop_yield ~ N + P + K + rainfall + soil_OC)
nrm_summary(pl)

Fit a response curve to NRM data

Description

Fits quadratic or nonlinear response curves relating an input (e.g. fertiliser rate) to a response variable (e.g. crop yield), commonly used in long-term fertiliser trial analysis.

Usage

nrm_response_curve(
  data,
  input_var,
  response_var,
  type = c("quadratic", "linear", "mitscherlich")
)

Arguments

Value

A list of class "nrm_response_curve" with:

model

The fitted model object.

type

The curve type used.

optimum

Estimated optimum input level (for quadratic).

r_squared

Model R-squared.

Examples

data(nrm_example)
rc <- nrm_response_curve(nrm_example,
        input_var = "N", response_var = "crop_yield", type = "quadratic")
nrm_plot(rc)

Structural Equation Modelling (SEM)

Description

Fits a Structural Equation Model (SEM) using sem. Intended for path analysis of interlinked Natural Resource Management (NRM) processes (e.g. soil organic carbon mediating the effect of fertiliser on yield).

Usage

nrm_sem(data, model, ...)

Arguments

Value

A list of class "nrm_sem" containing:

model

The fitted lavaan object.

fit_indices

Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardised Root Mean Square Residual (SRMR).

parameter_estimates

Standardised path coefficients.

References

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. doi:10.18637/jss.v048.i02

Examples

if (requireNamespace("lavaan", quietly = TRUE)) {
  model_syn <- "
    crop_yield ~ N + soil_OC
    soil_OC    ~ biomass + N
  "
  data(nrm_example)
  sem_res <- nrm_sem(nrm_example, model = model_syn)
  nrm_summary(sem_res)
}

Sen's slope estimator

Description

Computes Sen's slope as a robust, nonparametric estimate of the linear trend magnitude in a time series.

Usage

nrm_sens_slope(data, time_var = "year", value_var)

Arguments

Value

A list of class "nrm_sens_slope" with components:

slope

Sen's slope (units of value_var per time step).

intercept

Estimated intercept.

conf_int

95 % confidence interval for the slope.

raw

Full output from sens.slope.

References

Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63, 1379–1389.

Examples

data(nrm_example)
ss <- nrm_sens_slope(nrm_example, time_var = "year",
                     value_var = "crop_yield")
print(ss)

Structural break detection

Description

Detects structural breakpoints in a time series using the Bai-Perron framework implemented in strucchange.

Usage

nrm_structural_break(data, time_var = "year", value_var, max_breaks = 5)

Arguments

Value

A list of class "nrm_structural_break" with components:

n_breaks

Number of detected breaks.

break_dates

Time values at which breaks occur (extracted from time_var).

raw

Full breakpoints object.

References

Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18, 1–22.

Examples

data(nrm_example)
sb <- nrm_structural_break(nrm_example, time_var = "year",
                            value_var = "crop_yield")
print(sb)

Generic summary for NRMstatsML objects

Description

Prints a concise summary for any NRMstatsML result object.

Usage

nrm_summary(object, ...)

Arguments

Value

The object invisibly.

Comprehensive trend analysis for NRM time series

Description

Runs Mann-Kendall test, Sen's slope estimator, and optional structural-break detection on a univariate time series extracted from a data frame.

Usage

nrm_trend(data, time_var = "year", value_var, breaks = TRUE, alpha = 0.05)

Arguments

Value

A list of class "nrm_trend" with components:

mann_kendall

Output of nrm_mann_kendall.

sens_slope

Output of nrm_sens_slope.

structural_breaks

Output of nrm_structural_break or NULL when breaks = FALSE.

data

The data argument, for downstream use.

call

The matched call.

See Also

nrm_mann_kendall, nrm_sens_slope, nrm_structural_break

Examples

data(nrm_example)
result <- nrm_trend(nrm_example, time_var = "year", value_var = "crop_yield")
print(result)
nrm_plot(result)

Uncertainty analysis for NRM model outputs

Description

Runs bootstrap resampling or Monte Carlo simulation to quantify uncertainty around a statistic of interest computed on NRM data.

Usage

nrm_uncertainty(
  data,
  stat_fn,
  method = "bootstrap",
  n_iter = 1000L,
  alpha = 0.05,
  ...
)

Arguments

Value

A list of class "nrm_uncertainty" with:

mean

Mean of the resampled statistic.

ci

Confidence interval at 1 - alpha level.

sd

Standard deviation of the resampled statistic.

method

Method used.

Examples

data(nrm_example)
unc <- nrm_uncertainty(nrm_example,
         stat_fn = function(d) mean(d$crop_yield),
         method = "bootstrap", n_iter = 500)
print(unc)