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  "Title": "Model Evaluation and Analysis",
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  "Date": "2026-01-08",
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  "Description": "Analyses species distribution models and evaluates their\nperformance. It includes functions for variation partitioning,\nextracting variable importance, computing several metrics of\nmodel discrimination and calibration performance, optimizing\nprediction thresholds based on a number of criteria, performing\nmultivariate environmental similarity surface (MESS) analysis,\nand displaying various analytical plots. Initially described in\nBarbosa et al. (2013) <doi:10.1111/ddi.12100>.",
  "LazyLoad": "yes",
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  "License": "GPL-3",
  "URL": "http://modeva.r-forge.r-project.org/",
  "BugReports": "https://github.com/AMBarbosa/modEvA/issues",
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  "Repository": "https://ambarbosa.r-universe.dev",
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    "arrangePlots",
    "AUC",
    "Boyce",
    "confusionLabel",
    "confusionMatrix",
    "Dsquared",
    "errorMeasures",
    "evaluate",
    "evenness",
    "getBins",
    "getModEqn",
    "getThreshold",
    "HLfit",
    "inputMunch",
    "logLike",
    "lollipop",
    "MESS",
    "MillerCalib",
    "mod2obspred",
    "modEvAmethods",
    "multModEv",
    "OA",
    "optiPair",
    "optiThresh",
    "plotCoeffs",
    "plotGLM",
    "predDensity",
    "predPlot",
    "prevalence",
    "pseudoRsq",
    "ptsrast2obspred",
    "quantReclass",
    "range01",
    "RMSE",
    "RsqGLM",
    "similarity",
    "standard01",
    "threshMeasures",
    "varImp",
    "varPart"
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      "title": "Rotifer distribution models",
      "object": "rotif.mods",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    }
  ],
  "_help": [
    {
      "page": "modEvA-package",
      "title": "Model Evaluation and Analysis",
      "topics": [
        "modEvA-package",
        "modEvA"
      ]
    },
    {
      "page": "applyThreshold",
      "title": "Apply threshold(s) to model predictions",
      "topics": [
        "applyThreshold"
      ]
    },
    {
      "page": "arrangePlots",
      "title": "Arrange plots",
      "topics": [
        "arrangePlots"
      ]
    },
    {
      "page": "AUC",
      "title": "Area Under the Curve",
      "topics": [
        "AUC"
      ]
    },
    {
      "page": "Boyce",
      "title": "Boyce Index",
      "topics": [
        "Boyce"
      ]
    },
    {
      "page": "confusionLabel",
      "title": "Label predictions according to their confusion matrix category",
      "topics": [
        "confusionLabel"
      ]
    },
    {
      "page": "confusionMatrix",
      "title": "Confusion matrix",
      "topics": [
        "confusionMatrix"
      ]
    },
    {
      "page": "Dsquared",
      "title": "Explained deviance",
      "topics": [
        "Dsquared"
      ]
    },
    {
      "page": "errorMeasures",
      "title": "Measures of model prediction error.",
      "topics": [
        "errorMeasures"
      ]
    },
    {
      "page": "evaluate",
      "title": "Evaluate a model based on the elements of a confusion matrix.",
      "topics": [
        "evaluate"
      ]
    },
    {
      "page": "evenness",
      "title": "Evenness in a binary vector.",
      "topics": [
        "evenness"
      ]
    },
    {
      "page": "getBins",
      "title": "Get bins of continuous values.",
      "topics": [
        "getBins"
      ]
    },
    {
      "page": "getModEqn",
      "title": "Get model equation",
      "topics": [
        "getModEqn"
      ]
    },
    {
      "page": "getThreshold",
      "title": "Prediction threshold for a given criterion",
      "topics": [
        "getThreshold"
      ]
    },
    {
      "page": "HLfit",
      "title": "Hosmer-Lemeshow goodness of fit",
      "topics": [
        "HLfit"
      ]
    },
    {
      "page": "inputMunch",
      "title": "Munch inputs into 'obs' and 'pred' vectors",
      "topics": [
        "inputMunch"
      ]
    },
    {
      "page": "logLike",
      "title": "Log-likelihood",
      "topics": [
        "logLike"
      ]
    },
    {
      "page": "lollipop",
      "title": "Lollipop chart",
      "topics": [
        "lollipop"
      ]
    },
    {
      "page": "MESS",
      "title": "Multivariate Environmental Similarity Surfaces based on a data frame",
      "topics": [
        "MESS"
      ]
    },
    {
      "page": "MillerCalib",
      "title": "Miller's calibration satistics for logistic regression models",
      "topics": [
        "MillerCalib"
      ]
    },
    {
      "page": "mod2obspred",
      "title": "Extract observed and predicted, or predictor, values from a model object.",
      "topics": [
        "mod2obspred"
      ]
    },
    {
      "page": "modEvAmethods",
      "title": "Methods implemented in modEvA functions",
      "topics": [
        "modEvAmethods"
      ]
    },
    {
      "page": "multModEv",
      "title": "Multiple model evaluation",
      "topics": [
        "multModEv"
      ]
    },
    {
      "page": "OA",
      "title": "Overlap Analysis",
      "topics": [
        "OA"
      ]
    },
    {
      "page": "optiPair",
      "title": "Optimize the classification threshold for a pair of related model evaluation measures.",
      "topics": [
        "optiPair"
      ]
    },
    {
      "page": "optiThresh",
      "title": "Optimize threshold for model evaluation.",
      "topics": [
        "optiThresh"
      ]
    },
    {
      "page": "plotCoeffs",
      "title": "Plot model coefficients with confidence intervals",
      "topics": [
        "plotCoeffs"
      ]
    },
    {
      "page": "plotGLM",
      "title": "Plot a generalized linear model",
      "topics": [
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      ]
    },
    {
      "page": "predDensity",
      "title": "Plot the density of predicted or predictor values for presences and absences (or background)",
      "topics": [
        "predDensity"
      ]
    },
    {
      "page": "predPlot",
      "title": "Plot predicted values for presences and absences, optionally classified according to a prediction threshold.",
      "topics": [
        "predPlot"
      ]
    },
    {
      "page": "prevalence",
      "title": "Prevalence",
      "topics": [
        "prevalence"
      ]
    },
    {
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      "title": "Pseudo-R-squared measures for binary-response models",
      "topics": [
        "pseudoRsq"
      ]
    },
    {
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      "title": "Observed and predicted values from presence points and a raster map.",
      "topics": [
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      ]
    },
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      ]
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      "topics": [
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