{
  "_id": "6a156106acfb0bcc41d60e78",
  "Package": "soilFlux",
  "Title": "Physics-Informed Neural Networks for Soil Water Retention Curves",
  "Version": "0.1.5",
  "Authors@R": "person(\n\"Hugo\", \"Rodrigues\",\nemail = \"rodrigues.machado.hugo@gmail.com\",\nrole  = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8070-8126\")\n)",
  "Description": "Implements a physics-informed one-dimensional\nconvolutional neural network (CNN1D-PINN) for estimating the\ncomplete soil water retention curve (SWRC) as a continuous\nfunction of matric potential, from soil texture, organic\ncarbon, bulk density, and depth. The network architecture\nensures strict monotonic decrease of volumetric water content\nwith increasing suction by construction, through cumulative\nintegration of non-negative slope outputs (monotone integral\narchitecture). Four physics-based residual constraints adapted\nfrom Norouzi et al. (2025) <doi:10.1029/2024WR038149> are\nembedded in the loss function: (S1) linearity at the dry end\n(pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3)\nnon-positivity at pF = 7.6; and (S4) a near-zero derivative in\nthe saturated plateau region (pF in [-2, -0.3]). Includes tools\nfor data preparation, model training, dense prediction,\nperformance metrics, texture classification, and\npublication-quality visualisation.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "SystemRequirements": "Python (>= 3.8), TensorFlow (>= 2.14), Keras (>=\n3.0)",
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  "URL": "https://github.com/HugoMachadoRodrigues/soilFlux,\nhttps://hugomachadorodrigues.github.io/soilFlux/",
  "BugReports": "https://github.com/HugoMachadoRodrigues/soilFlux/issues",
  "Config/pak/sysreqs": "libicu-dev libpng-dev python3",
  "Repository": "https://hugomachadorodrigues.r-universe.dev",
  "Date/Publication": "2026-03-23 18:59:56 UTC",
  "RemoteUrl": "https://github.com/hugomachadorodrigues/soilflux",
  "RemoteRef": "HEAD",
  "RemoteSha": "12219c6da403cb564b6f1648191d468c4dab931d",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-26 08:55:49 UTC",
    "User": "root"
  },
  "Author": "Hugo Rodrigues [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8070-8126>)",
  "Maintainer": "Hugo Rodrigues <rodrigues.machado.hugo@gmail.com>",
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  "_user": "hugomachadorodrigues",
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  "_created": "2026-05-26T08:55:49.000Z",
  "_published": "2026-05-26T08:59:50.749Z",
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  "_commit": {
    "id": "12219c6da403cb564b6f1648191d468c4dab931d",
    "author": "Hugo Rodrigues <rodrigues.h@ufl.edu>",
    "committer": "Hugo Rodrigues <rodrigues.h@ufl.edu>",
    "message": "docs: expand retention-table to show all 8 KSSL tension points\n\nReplace the 3-row raw-column table with the full 8-point table matching\nswrc_example (pF 0–7), and add a note explaining that the number of\nreturned columns varies by query and that swrc_example uses 8 standard\ntensions.\n",
    "time": 1774292396
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  "_maintainer": {
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    "login": "hugomachadorodrigues",
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    "twitter": "@Hugo_MRodrigues",
    "description": "Research Soil Scientist | Soil Health Modeling, Digital Soil Mapping & AI | Spectroscopy",
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  "_exports": [
    "add_texture",
    "apply_minmax",
    "build_residual_sets",
    "build_swrc_model",
    "classify_texture",
    "compute_physics_loss",
    "evaluate_swrc",
    "fit_minmax",
    "fit_swrc",
    "fix_bd_units",
    "head_from_pf",
    "head_normalize",
    "invert_minmax",
    "load_swrc_model",
    "make_obs_matrices",
    "make_profile_array",
    "norouzi_lambdas",
    "parse_depth",
    "parse_depth_column",
    "pf_from_head",
    "pf_normalize",
    "plot_pred_obs",
    "plot_swrc",
    "plot_swrc_metrics",
    "plot_training_history",
    "predict_swrc",
    "predict_swrc_dense",
    "predict_theta_s",
    "prepare_swrc_data",
    "residual_to_tensors",
    "save_swrc_model",
    "swrc_metrics",
    "swrc_metrics_by_group",
    "swrc_model_exists",
    "texture_triangle",
    "theta_unit_factor"
  ],
  "_datasets": [
    {
      "name": "swrc_example",
      "title": "Example soil water retention dataset",
      "object": "swrc_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "PEDON_ID",
        "sand_total",
        "silt",
        "clay",
        "soc",
        "bd",
        "sand_vf",
        "sand_f",
        "sand_m",
        "sand_c",
        "matric_head",
        "water_content",
        "depth",
        "Texture"
      ],
      "rows": 4800,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "add_texture",
      "title": "Add texture classification column to a data frame",
      "topics": [
        "add_texture"
      ]
    },
    {
      "page": "apply_minmax",
      "title": "Apply a fitted min-max scaler to a data frame",
      "topics": [
        "apply_minmax"
      ]
    },
    {
      "page": "build_residual_sets",
      "title": "Build physics residual point sets (S1 – S4)",
      "topics": [
        "build_residual_sets"
      ]
    },
    {
      "page": "build_swrc_model",
      "title": "Build the CNN1D monotone-integral SWRC model",
      "topics": [
        "build_swrc_model"
      ]
    },
    {
      "page": "classify_texture",
      "title": "Classify soil texture according to the USDA system",
      "topics": [
        "classify_texture"
      ]
    },
    {
      "page": "data_prep",
      "title": "Data preparation for CNN1D SWRC modelling",
      "topics": [
        "data_prep"
      ]
    },
    {
      "page": "evaluate_swrc",
      "title": "Compute metrics from a swrc_fit on new data",
      "topics": [
        "evaluate_swrc"
      ]
    },
    {
      "page": "fit_minmax",
      "title": "Fit a min-max scaler from a training data frame",
      "topics": [
        "fit_minmax"
      ]
    },
    {
      "page": "fit_swrc",
      "title": "Fit a physics-informed CNN1D SWRC model",
      "topics": [
        "fit_swrc"
      ]
    },
    {
      "page": "fix_bd_units",
      "title": "Detect and correct bulk-density units",
      "topics": [
        "fix_bd_units"
      ]
    },
    {
      "page": "head_from_pf",
      "title": "Convert pF to matric head (cm)",
      "topics": [
        "head_from_pf"
      ]
    },
    {
      "page": "head_normalize",
      "title": "Normalise matric head (cm) to the pF domain",
      "topics": [
        "head_normalize"
      ]
    },
    {
      "page": "invert_minmax",
      "title": "Invert a min-max scaling transformation",
      "topics": [
        "invert_minmax"
      ]
    },
    {
      "page": "io",
      "title": "Save and load fitted SWRC models",
      "topics": [
        "io"
      ]
    },
    {
      "page": "load_swrc_model",
      "title": "Load a previously saved SWRC model from disk",
      "topics": [
        "load_swrc_model"
      ]
    },
    {
      "page": "metrics",
      "title": "Performance metrics for SWRC models",
      "topics": [
        "metrics"
      ]
    },
    {
      "page": "model",
      "title": "CNN1D monotone-integral model architecture",
      "topics": [
        "model"
      ]
    },
    {
      "page": "norouzi_lambdas",
      "title": "Return default Norouzi et al. (2025) loss weights (lambdas)",
      "topics": [
        "norouzi_lambdas"
      ]
    },
    {
      "page": "parse_depth",
      "title": "Parse a soil depth string into midpoint and label",
      "topics": [
        "parse_depth"
      ]
    },
    {
      "page": "parse_depth_column",
      "title": "Parse depth column in a data frame",
      "topics": [
        "parse_depth_column"
      ]
    },
    {
      "page": "pf_from_head",
      "title": "Convert matric head (cm) to pF",
      "topics": [
        "pf_from_head"
      ]
    },
    {
      "page": "pf_normalize",
      "title": "Normalise pF values to [0, 1]",
      "topics": [
        "pf_normalize"
      ]
    },
    {
      "page": "physics",
      "title": "Physics-informed constraints for SWRC modelling",
      "topics": [
        "physics"
      ]
    },
    {
      "page": "plot_pred_obs",
      "title": "Plot predicted vs. observed water content",
      "topics": [
        "plot_pred_obs"
      ]
    },
    {
      "page": "plot_swrc",
      "title": "Plot soil water retention curves (SWRC)",
      "topics": [
        "plot_swrc"
      ]
    },
    {
      "page": "plot_swrc_metrics",
      "title": "Plot model performance metric comparison",
      "topics": [
        "plot_swrc_metrics"
      ]
    },
    {
      "page": "plot_training_history",
      "title": "Plot training loss history",
      "topics": [
        "plot_training_history"
      ]
    },
    {
      "page": "plots",
      "title": "Publication-quality plots for SWRC analysis",
      "topics": [
        "plots"
      ]
    },
    {
      "page": "predict",
      "title": "Prediction from fitted SWRC models",
      "topics": [
        "predict"
      ]
    },
    {
      "page": "predict_swrc",
      "title": "Predict water content at specific pF or matric-head values",
      "topics": [
        "predict_swrc"
      ]
    },
    {
      "page": "predict_swrc_dense",
      "title": "Predict dense SWRC curves for a set of soil profiles",
      "topics": [
        "predict_swrc_dense"
      ]
    },
    {
      "page": "predict_theta_s",
      "title": "Extract saturated water content (theta_s) from covariates",
      "topics": [
        "predict_theta_s"
      ]
    },
    {
      "page": "predict.swrc_fit",
      "title": "Predict method for swrc_fit",
      "topics": [
        "predict.swrc_fit"
      ]
    },
    {
      "page": "prepare_swrc_data",
      "title": "Prepare a soil data frame for SWRC modelling",
      "topics": [
        "prepare_swrc_data"
      ]
    },
    {
      "page": "print.swrc_fit",
      "title": "Print method for swrc_fit",
      "topics": [
        "print.swrc_fit"
      ]
    },
    {
      "page": "save_swrc_model",
      "title": "Save a fitted SWRC model to disk",
      "topics": [
        "save_swrc_model"
      ]
    },
    {
      "page": "scale",
      "title": "Min-max feature scaling",
      "topics": [
        "scale"
      ]
    },
    {
      "page": "summary.swrc_fit",
      "title": "Summary method for swrc_fit",
      "topics": [
        "summary.swrc_fit"
      ]
    },
    {
      "page": "swrc_example",
      "title": "Example soil water retention dataset",
      "topics": [
        "swrc_example"
      ]
    },
    {
      "page": "swrc_metrics",
      "title": "Compute regression metrics for SWRC predictions",
      "topics": [
        "swrc_metrics"
      ]
    },
    {
      "page": "swrc_metrics_by_group",
      "title": "Compute regression metrics by group",
      "topics": [
        "swrc_metrics_by_group"
      ]
    },
    {
      "page": "swrc_model_exists",
      "title": "Check whether a model directory contains a valid saved model",
      "topics": [
        "swrc_model_exists"
      ]
    },
    {
      "page": "texture",
      "title": "USDA soil texture classification",
      "topics": [
        "texture"
      ]
    },
    {
      "page": "texture_triangle",
      "title": "Plot a USDA soil texture triangle (ternary diagram)",
      "topics": [
        "texture_triangle"
      ]
    },
    {
      "page": "theta_unit_factor",
      "title": "Detect theta unit scale factor",
      "topics": [
        "theta_unit_factor"
      ]
    },
    {
      "page": "train",
      "title": "Training the CNN1D SWRC model",
      "topics": [
        "train"
      ]
    },
    {
      "page": "utils",
      "title": "Utility functions for soilFlux",
      "topics": [
        "utils"
      ]
    }
  ],
  "_pkglogo": "https://github.com/hugomachadorodrigues/soilflux/raw/HEAD/man/figures/logo.png",
  "_readme": "https://github.com/hugomachadorodrigues/soilflux/raw/HEAD/README.md",
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  "_vignettes": [
    {
      "source": "introduction.Rmd",
      "filename": "introduction.html",
      "title": "Introduction to soilFlux",
      "author": "Hugo Rodrigues",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "What is the SWRC?",
        "Key features",
        "Quick-start",
        "Installation",
        "Minimal example",
        "Data preparation",
        "Expected columns",
        "Unit handling",
        "Model architecture",
        "Monotone integral layer",
        "Inputs to the model",
        "Physics constraints",
        "Saving and loading models",
        "Visualisation",
        "SWRC curves",
        "Predicted vs. observed",
        "Training history",
        "Texture classification",
        "Session information",
        "References"
      ],
      "created": "2026-03-12 21:49:56",
      "modified": "2026-03-13 03:38:01",
      "commits": 3
    },
    {
      "source": "pedometric-workflow.Rmd",
      "filename": "pedometric-workflow.html",
      "title": "Pedometric Workflow: KSSL Data, Spline Harmonisation, and SWRC Fitting",
      "author": "Hugo Rodrigues",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Why harmonise horizons?",
        "Required packages",
        "The mass-preserving spline — illustrated",
        "Fetching KSSL data — North Carolina",
        "Horizon table structure",
        "Visualise raw profiles with aqp",
        "Mass-preserving spline harmonisation on KSSL",
        "Prepare input for mpspline2",
        "Apply splines profile-by-profile",
        "Compare raw vs. splined: one profile",
        "Exploring the harmonised dataset",
        "Distribution of soil properties by depth",
        "Observed retention curves by depth",
        "Texture triangle",
        "Reshaping water retention and preparing for soilFlux",
        "Fitting the soilFlux model",
        "Train / validation / test split",
        "Fit",
        "Visualising predictions",
        "Full SWRC curves — depth × texture",
        "Predicted vs. observed scatter",
        "Vertical profile — FC and WP at standard depths",
        "Exporting the continuous SWRC data",
        "Save and reload the model",
        "Summary",
        "References"
      ],
      "created": "2026-03-22 22:02:00",
      "modified": "2026-03-23 18:59:56",
      "commits": 14
    }
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  "_indexed": true,
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