Package: NRMstatsML 0.1.4

NRMstatsML: Statistical and Machine Learning Engine for Long-Term Natural Resource Management Data

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>.

Authors:Sadikul Islam [aut, cre, cph]

NRMstatsML_0.1.4.tar.gz
NRMstatsML_0.1.4.zip(r-4.7-any)NRMstatsML_0.1.4.zip(r-4.6-any)NRMstatsML_0.1.4.zip(r-4.5-any)
NRMstatsML_0.1.4.tgz(r-4.6-any)NRMstatsML_0.1.4.tgz(r-4.5-any)
NRMstatsML_0.1.4.tar.gz(r-4.7-any)NRMstatsML_0.1.4.tar.gz(r-4.6-any)
NRMstatsML_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
NRMstatsML/json (API)

# Install 'NRMstatsML' in R:
install.packages('NRMstatsML', repos = c('https://sadikulislamiasri-hub.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 241 downloads 21 exports 99 dependencies

Last updated from:f2bfeca18b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK146
source / vignettesOK227
linux-releaseOK171
macos-releaseOK84
macos-oldrelOK98
windows-develOK71
windows-releaseOK107
windows-oldrelOK76
wasm-releaseOK127

Exports:nrm_arimanrm_automlnrm_benchmarknrm_bootstrapnrm_data_checknrm_didnrm_forecastnrm_mann_kendallnrm_monte_carlonrm_multivariatenrm_optimize_inputnrm_panelnrm_plotnrm_plsnrm_response_curvenrm_semnrm_sens_slopenrm_structural_breaknrm_summarynrm_trendnrm_uncertainty

Dependencies:bdsmatrixbootcaretclasscliclockcodetoolscollapsecolorspacecpp11data.tablediagramdigestdplyre1071extraDistrfarverforeachforecastFormulafracdifffuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKendallKernSmoothlabelinglatticelavalavaanlifecyclelistenvlmtestlubridatemagrittrMASSMatrixmaxLikmiscToolsmnormtModelMetricsnlmennetnumDerivparallellypbivnormpillarpkgconfigplmplsplyrpROCprodlimprogressrproxypurrrquadprogR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrecipesreshape2rlangrpartS7sandwichscalesshapesparsevctrsSQUAREMstringistringrstrucchangesurvivaltibbletidyrtidyselecttimechangetimeDatetrendtzdburcautf8vctrsviridisLitewithrzoo

Advanced Modelling Workflows with NRMstatsML
Overview | 1. Loading Data from CSV | 2. Combining Trend Detection and Uncertainty | 3. Multi-Variable Response Surface | 4. Uncertainty-Adjusted Forecasting | 5. Custom stat_fn Closures | Example A — R-squared of an OLS model | Example B — Optimum N from a quadratic response curve | Example C — Mann-Kendall tau for soil OC | 6. Multi-Metric Model Comparison | 7. Complete Reproducible Pipeline | Session Info
Rendered fromadvanced-workflows.Rmdusingknitr::rmarkdown

Last update: 2026-06-07
Started: 2026-06-07

Getting Started with NRMstatsML
Introduction | Installation | Example Data | Module 1 — Trend Analysis (trendML) | Mann-Kendall test and Sen's slope | Visualise the trend | Individual components | Module 2 — Multivariate System Modelling (multiSysML) | Scaled OLS regression | Partial Least Squares (PLS) | Module 3 — Response Curve & Optimisation (responseML) | Economic optimum | Module 4 — Time Series Forecasting (tsML) | Module 5 — Panel Data & Treatment Effects (panelML) | Module 6 — Uncertainty & Sensitivity Analysis (uncertaintyML) | Module 7 — AutoML & Model Benchmarking (autoML) | Benchmarking on a hold-out set | Recommended Workflow | Session Info
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-06-07
Started: 2026-06-07