{
  "_id": "6a25df54794753ddbc3e1626",
  "Package": "NRMstatsML",
  "Type": "Package",
  "Title": "Statistical and Machine Learning Engine for Long-Term Natural\nResource Management Data",
  "Version": "0.1.4",
  "Authors@R": "person(\ngiven = \"Sadikul\",\nfamily = \"Islam\",\nemail = \"sadikul.islamiasri@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0003-2924-7122\")\n)",
  "Description": "A comprehensive toolkit for statistical and machine\nlearning-based analysis of long-term Natural Resource\nManagement (NRM) datasets. Integrates formula-driven\napproaches, statistical inference, and machine learning (ML)\nmodels for advanced analytics. Modules cover trend and\nstructural analysis (Mann-Kendall test, slope estimation, Chow\ntest, structural break detection), multivariate system\nmodelling (Partial Least Squares (PLS), Structural Equation\nModelling (SEM)), response curve optimisation, time-series\nforecasting (Autoregressive Integrated Moving Average (ARIMA),\nhybrid models), panel data and treatment effects\n(Difference-in-Differences (DiD), causal machine learning),\nuncertainty and sensitivity analysis (bootstrap, Monte Carlo,\nBayesian), and automated model selection and performance\ncomparison. Designed for long-term datasets covering soil,\nwater, crop, and climate domains. Key references: Mann and\nKendall (1945) <doi:10.2307/1907187>; Sen (1968)\n<doi:10.1080/01621459.1968.10480934>; Bai and Perron (2003)\n<doi:10.1002/jae.659>; Rosseel (2012)\n<doi:10.18637/jss.v048.i02>; Croissant and Millo (2008)\n<doi:10.18637/jss.v027.i02>.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Language": "en-US",
  "VignetteBuilder": "knitr",
  "RoxygenNote": "7.3.3",
  "Config/testthat/edition": "3",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-07 21:11:40 UTC",
    "User": "root"
  },
  "Author": "Sadikul Islam [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0003-2924-7122>)",
  "Maintainer": "Sadikul Islam <sadikul.islamiasri@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-06-07 18:30:08 UTC",
  "RemoteUrl": "https://github.com/cran/NRMstatsML",
  "RemoteRef": "HEAD",
  "RemoteSha": "f2bfeca18b07c45d9f58d1fb43c8aae4231e53a8",
  "MD5sum": "f10481f9d25acf2ad16e52d52ba5ffc9",
  "_user": "cran",
  "_type": "src",
  "_file": "NRMstatsML_0.1.4.tar.gz",
  "_fileid": "e11bca4ea61df8ff47fd9eb10d0340e9edc84ca8766937361dc931e3961feeb2",
  "_filesize": 462888,
  "_sha256": "e11bca4ea61df8ff47fd9eb10d0340e9edc84ca8766937361dc931e3961feeb2",
  "_created": "2026-06-07T21:11:40.000Z",
  "_published": "2026-06-07T21:15:00.336Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 79992316298,
      "time": 162,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7468041350"
    },
    {
      "job": 79992316294,
      "time": 171,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7468042639"
    },
    {
      "job": 79992053261,
      "time": 232,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7468019602"
    },
    {
      "job": 79992316296,
      "time": 160,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7468041050"
    }
  ],
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/27104853912",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/NRMstatsML",
  "_commit": {
    "id": "f2bfeca18b07c45d9f58d1fb43c8aae4231e53a8",
    "author": "Sadikul Islam <sadikul.islamiasri@gmail.com>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.1.4\n",
    "time": 1780857008
  },
  "_maintainer": {
    "name": "Sadikul Islam",
    "email": "sadikul.islamiasri@gmail.com",
    "orcid": "0000-0003-2924-7122"
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 4.1.0",
      "role": "Depends"
    },
    {
      "package": "Kendall",
      "version": ">= 2.2",
      "role": "Imports"
    },
    {
      "package": "trend",
      "version": ">= 1.1.4",
      "role": "Imports"
    },
    {
      "package": "strucchange",
      "version": ">= 1.5.3",
      "role": "Imports"
    },
    {
      "package": "plm",
      "version": ">= 2.6.0",
      "role": "Imports"
    },
    {
      "package": "forecast",
      "version": ">= 8.20",
      "role": "Imports"
    },
    {
      "package": "lavaan",
      "version": ">= 0.6.12",
      "role": "Imports"
    },
    {
      "package": "pls",
      "version": ">= 2.8.0",
      "role": "Imports"
    },
    {
      "package": "caret",
      "version": ">= 6.0.93",
      "role": "Imports"
    },
    {
      "package": "boot",
      "version": ">= 1.3.28",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "version": ">= 3.4.0",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "version": ">= 1.1.0",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "utils",
      "role": "Imports"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    },
    {
      "package": "keras",
      "role": "Suggests"
    },
    {
      "package": "tensorflow",
      "role": "Suggests"
    },
    {
      "package": "BayesianTools",
      "role": "Suggests"
    },
    {
      "package": "sensitivity",
      "role": "Suggests"
    },
    {
      "package": "mboost",
      "role": "Suggests"
    },
    {
      "package": "mlr3",
      "role": "Suggests"
    },
    {
      "package": "covr",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2026-23",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "0.1.4",
      "date": "2026-06-07"
    }
  ],
  "_stars": 0,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/NRMstatsML"
  },
  "_searchresults": 0,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/NRMstatsML.html",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
  ],
  "_cranurl": false,
  "_releases": [
    {
      "version": "0.1.4",
      "date": "2026-06-07"
    }
  ],
  "_exports": [
    "nrm_arima",
    "nrm_automl",
    "nrm_benchmark",
    "nrm_bootstrap",
    "nrm_data_check",
    "nrm_did",
    "nrm_forecast",
    "nrm_mann_kendall",
    "nrm_monte_carlo",
    "nrm_multivariate",
    "nrm_optimize_input",
    "nrm_panel",
    "nrm_plot",
    "nrm_pls",
    "nrm_response_curve",
    "nrm_sem",
    "nrm_sens_slope",
    "nrm_structural_break",
    "nrm_summary",
    "nrm_trend",
    "nrm_uncertainty"
  ],
  "_datasets": [
    {
      "name": "nrm_example",
      "title": "Example long-term NRM dataset",
      "object": "nrm_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "year",
        "treatment",
        "crop_yield",
        "soil_OC",
        "N",
        "P",
        "K",
        "rainfall",
        "runoff",
        "biomass"
      ],
      "rows": 20,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "NRMstatsML-package",
      "title": "NRMstatsML: Statistical and Machine Learning Engine for Long-Term NRM Data",
      "topics": [
        "NRMstatsML-package",
        "NRMstatsML"
      ]
    },
    {
      "page": "nrm_arima",
      "title": "ARIMA model for NRM time series",
      "topics": [
        "nrm_arima"
      ]
    },
    {
      "page": "nrm_automl",
      "title": "Automated model selection and tuning",
      "topics": [
        "nrm_automl"
      ]
    },
    {
      "page": "nrm_benchmark",
      "title": "Benchmark model metrics on a hold-out test set",
      "topics": [
        "nrm_benchmark"
      ]
    },
    {
      "page": "nrm_bootstrap",
      "title": "Bootstrap uncertainty estimation",
      "topics": [
        "nrm_bootstrap"
      ]
    },
    {
      "page": "nrm_data_check",
      "title": "Validate and summarise an NRM dataset",
      "topics": [
        "nrm_data_check"
      ]
    },
    {
      "page": "nrm_did",
      "title": "Difference-in-Differences (DiD) estimator",
      "topics": [
        "nrm_did"
      ]
    },
    {
      "page": "nrm_example",
      "title": "Example long-term NRM dataset",
      "topics": [
        "nrm_example"
      ]
    },
    {
      "page": "nrm_forecast",
      "title": "Forecast NRM time series",
      "topics": [
        "nrm_forecast"
      ]
    },
    {
      "page": "nrm_mann_kendall",
      "title": "Mann-Kendall trend test",
      "topics": [
        "nrm_mann_kendall"
      ]
    },
    {
      "page": "nrm_monte_carlo",
      "title": "Monte Carlo uncertainty simulation",
      "topics": [
        "nrm_monte_carlo"
      ]
    },
    {
      "page": "nrm_multivariate",
      "title": "Multivariate regression for Natural Resource Management systems",
      "topics": [
        "nrm_multivariate"
      ]
    },
    {
      "page": "nrm_optimize_input",
      "title": "Optimise input level for maximum response",
      "topics": [
        "nrm_optimize_input"
      ]
    },
    {
      "page": "nrm_panel",
      "title": "Panel data regression for Natural Resource Management experiments",
      "topics": [
        "nrm_panel"
      ]
    },
    {
      "page": "nrm_plot",
      "title": "Generic plot for NRMstatsML objects",
      "topics": [
        "nrm_plot"
      ]
    },
    {
      "page": "nrm_pls",
      "title": "Partial Least Squares (PLS) regression",
      "topics": [
        "nrm_pls"
      ]
    },
    {
      "page": "nrm_response_curve",
      "title": "Fit a response curve to NRM data",
      "topics": [
        "nrm_response_curve"
      ]
    },
    {
      "page": "nrm_sem",
      "title": "Structural Equation Modelling (SEM)",
      "topics": [
        "nrm_sem"
      ]
    },
    {
      "page": "nrm_sens_slope",
      "title": "Sen's slope estimator",
      "topics": [
        "nrm_sens_slope"
      ]
    },
    {
      "page": "nrm_structural_break",
      "title": "Structural break detection",
      "topics": [
        "nrm_structural_break"
      ]
    },
    {
      "page": "nrm_summary",
      "title": "Generic summary for NRMstatsML objects",
      "topics": [
        "nrm_summary"
      ]
    },
    {
      "page": "nrm_trend",
      "title": "Comprehensive trend analysis for NRM time series",
      "topics": [
        "nrm_trend"
      ]
    },
    {
      "page": "nrm_uncertainty",
      "title": "Uncertainty analysis for NRM model outputs",
      "topics": [
        "nrm_uncertainty"
      ]
    }
  ],
  "_readme": "https://github.com/cran/NRMstatsML/raw/HEAD/README.md",
  "_rundeps": [
    "bdsmatrix",
    "boot",
    "caret",
    "class",
    "cli",
    "clock",
    "codetools",
    "collapse",
    "colorspace",
    "cpp11",
    "data.table",
    "diagram",
    "digest",
    "dplyr",
    "e1071",
    "extraDistr",
    "farver",
    "foreach",
    "forecast",
    "Formula",
    "fracdiff",
    "future",
    "future.apply",
    "generics",
    "ggplot2",
    "globals",
    "glue",
    "gower",
    "gtable",
    "hardhat",
    "ipred",
    "isoband",
    "iterators",
    "Kendall",
    "KernSmooth",
    "labeling",
    "lattice",
    "lava",
    "lavaan",
    "lifecycle",
    "listenv",
    "lmtest",
    "lubridate",
    "magrittr",
    "MASS",
    "Matrix",
    "maxLik",
    "miscTools",
    "mnormt",
    "ModelMetrics",
    "nlme",
    "nnet",
    "numDeriv",
    "parallelly",
    "pbivnorm",
    "pillar",
    "pkgconfig",
    "plm",
    "pls",
    "plyr",
    "pROC",
    "prodlim",
    "progressr",
    "proxy",
    "purrr",
    "quadprog",
    "R6",
    "rbibutils",
    "RColorBrewer",
    "Rcpp",
    "RcppArmadillo",
    "Rdpack",
    "recipes",
    "reshape2",
    "rlang",
    "rpart",
    "S7",
    "sandwich",
    "scales",
    "shape",
    "sparsevctrs",
    "SQUAREM",
    "stringi",
    "stringr",
    "strucchange",
    "survival",
    "tibble",
    "tidyr",
    "tidyselect",
    "timechange",
    "timeDate",
    "trend",
    "tzdb",
    "urca",
    "utf8",
    "vctrs",
    "viridisLite",
    "withr",
    "zoo"
  ],
  "_vignettes": [
    {
      "source": "advanced-workflows.Rmd",
      "filename": "advanced-workflows.html",
      "title": "Advanced Modelling Workflows with NRMstatsML",
      "engine": "knitr::rmarkdown",
      "headings": [
        "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"
      ],
      "created": "2026-06-07 18:30:08",
      "modified": "2026-06-07 18:30:08",
      "commits": 1
    },
    {
      "source": "getting-started.Rmd",
      "filename": "getting-started.html",
      "title": "Getting Started with NRMstatsML",
      "engine": "knitr::rmarkdown",
      "headings": [
        "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"
      ],
      "created": "2026-06-07 18:30:08",
      "modified": "2026-06-07 18:30:08",
      "commits": 1
    }
  ],
  "_score": 3,
  "_indexed": true,
  "_nocasepkg": "nrmstatsml",
  "_universes": [
    "cran"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.1.4",
      "date": "2026-06-07T21:14:21.000Z",
      "distro": "noble",
      "commit": "f2bfeca18b07c45d9f58d1fb43c8aae4231e53a8",
      "fileid": "d3a9d789ed19dc1d29eddc9cff8b6b5f071b449b71b22c7abf836d8be99257d6",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/27104853912"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.1.4",
      "date": "2026-06-07T21:14:28.000Z",
      "distro": "noble",
      "commit": "f2bfeca18b07c45d9f58d1fb43c8aae4231e53a8",
      "fileid": "36f6f686bad18af6afc534c59f6f690f908d062e132b81e12d9e2aecbe32aa36",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/27104853912"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.1.4",
      "date": "2026-06-07T21:14:35.000Z",
      "commit": "f2bfeca18b07c45d9f58d1fb43c8aae4231e53a8",
      "fileid": "6a1d5405b3812884d00391e9145a3e2429f8b5b87e42452630b608b5eef3c2fe",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/27104853912"
    }
  ]
}