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

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Documentation for package ‘NRMstatsML’ version 0.1.4

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

NRMstatsML-package	NRMstatsML: Statistical and Machine Learning Engine for Long-Term NRM Data
NRMstatsML	NRMstatsML: Statistical and Machine Learning Engine for Long-Term NRM Data
nrm_arima	ARIMA model for NRM time series
nrm_automl	Automated model selection and tuning
nrm_benchmark	Benchmark model metrics on a hold-out test set
nrm_bootstrap	Bootstrap uncertainty estimation
nrm_data_check	Validate and summarise an NRM dataset
nrm_did	Difference-in-Differences (DiD) estimator
nrm_example	Example long-term NRM dataset
nrm_forecast	Forecast NRM time series
nrm_mann_kendall	Mann-Kendall trend test
nrm_monte_carlo	Monte Carlo uncertainty simulation
nrm_multivariate	Multivariate regression for Natural Resource Management systems
nrm_optimize_input	Optimise input level for maximum response
nrm_panel	Panel data regression for Natural Resource Management experiments
nrm_plot	Generic plot for NRMstatsML objects
nrm_pls	Partial Least Squares (PLS) regression
nrm_response_curve	Fit a response curve to NRM data
nrm_sem	Structural Equation Modelling (SEM)
nrm_sens_slope	Sen's slope estimator
nrm_structural_break	Structural break detection
nrm_summary	Generic summary for NRMstatsML objects
nrm_trend	Comprehensive trend analysis for NRM time series
nrm_uncertainty	Uncertainty analysis for NRM model outputs