{
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  "Package": "douconca",
  "Type": "Package",
  "Title": "Double Constrained Correspondence Analysis for Trait-Environment\nAnalysis in Ecology",
  "Version": "1.2.2",
  "Authors@R": "c(person(given = \"Cajo J.F\", \nfamily = \"ter Braak\",\nemail = \"cajo.terbraak@wur.nl\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-0414-8745\")),\nperson(given = \"Bart-Jan\",\nfamily = \"van Rossum\",\nemail = \"bart-jan.vanrossum@wur.nl\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8673-2514\")))",
  "Description": "Double constrained correspondence analysis (dc-CA)\nanalyzes (multi-)trait (multi-)environment ecological data by\nusing library vegan and native R code. Throughout the two step\nalgorithm of ter Braak et al. (2018) is used. This algorithm\ncombines and extends community- (sample-) and species-level\nanalyses, i.e. the usual community weighted means (CWM)-based\nregression analysis and the species-level analysis of\nspecies-niche centroids (SNC)-based regression analysis. The\ntwo steps use canonical correspondence analysis to regress the\nabundance data on to the traits and (weighted) redundancy\nanalysis to regress the CWM of the orthonormalized traits on to\nthe environmental predictors. The function dc_CA has an option\nto divide the abundance data of a site by the site total,\ngiving equal site weights. This division has the advantage that\nthe multivariate analysis corresponds with an unweighted\n(multi-trait) community-level analysis, instead of being\nweighted. The first step of the algorithm uses vegan::cca. The\nsecond step uses douconca::wrda but vegan::rda if the site\nweights are equal. This version has a predict function. For\ndetails see ter Braak et al. 2018\n<doi:10.1007/s10651-017-0395-x>.",
  "URL": "https://github.com/CajoterBraak/douconca",
  "BugReports": "https://github.com/CajoterBraak/douconca/issues",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "NeedsCompilation": "no",
  "VignetteBuilder": "knitr",
  "Repository": "https://cajoterbraak.r-universe.dev",
  "Date/Publication": "2025-01-09 14:19:17 UTC",
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    "User": "root"
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  "Author": "Cajo J.F ter Braak [aut] (ORCID:\n<https://orcid.org/0000-0002-0414-8745>),\nBart-Jan van Rossum [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8673-2514>)",
  "Maintainer": "Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>",
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  "_created": "2026-05-08T07:06:22.000Z",
  "_published": "2026-05-22T14:10:06.007Z",
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    "name": "Bart-Jan van Rossum",
    "email": "bart-jan.vanrossum@wur.nl",
    "login": "bartjanvanrossum",
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    "orcid": "0000-0002-8673-2514"
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      "package": "permute",
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      "package": "rlang",
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  "_owner": "cajoterbraak",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_stars": 1,
  "_contributors": [
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      "user": "cajoterbraak",
      "count": 222,
      "uuid": 12693502
    }
  ],
  "_userbio": {
    "uuid": 12693502,
    "type": "user",
    "name": "Cajo ter Braak",
    "description": "Professor emeritus in multivariate analysis for community ecology and Bayesian computiing"
  },
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/douconca"
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/douconca.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
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    "extra/readme.md",
    "manual.pdf"
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  "_homeurl": "https://github.com/biometris/douconca",
  "_realowner": "biometris",
  "_cranurl": false,
  "_releases": [
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      "version": "1.2.0",
      "date": "2024-09-13"
    },
    {
      "version": "1.2.1",
      "date": "2024-09-25"
    },
    {
      "version": "1.2.2",
      "date": "2024-12-02"
    },
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      "date": "2025-05-09"
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      "date": "2025-10-14"
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    {
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      "date": "2026-02-23"
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  "_exports": [
    "anova_sites",
    "anova_species",
    "dc_CA",
    "fCWM_SNC",
    "getPlotdata",
    "plot_dcCA_CWM_SNC",
    "plot_species_scores_bk",
    "scores",
    "wrda"
  ],
  "_datasets": [
    {
      "name": "dune_trait_env",
      "title": "Dune meadow data with plant species traits and environmental variables",
      "object": "dune_trait_env",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "anova_sites",
      "title": "Utility function: community-level permutation test in Double Constrained Correspondence Analysis (dc-CA)",
      "topics": [
        "anova_sites"
      ]
    },
    {
      "page": "anova_species",
      "title": "Utility function: Species-level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)",
      "topics": [
        "anova_species"
      ]
    },
    {
      "page": "anova.dcca",
      "title": "Community- and Species-Level Permutation Test in Double Constrained Correspondence Analysis (dc-CA)",
      "topics": [
        "anova.dcca"
      ]
    },
    {
      "page": "anova.wrda",
      "title": "Permutation Test for weighted redundancy analysis",
      "topics": [
        "anova.wrda"
      ]
    },
    {
      "page": "coef.dcca",
      "title": "Coefficients of double-constrained correspondence analysis (dc-CA)",
      "topics": [
        "coef.dcca"
      ]
    },
    {
      "page": "dc_CA",
      "title": "Performs (weighted) double constrained correspondence analysis (dc-CA)",
      "topics": [
        "dc_CA"
      ]
    },
    {
      "page": "dune_trait_env",
      "title": "Dune meadow data with plant species traits and environmental variables",
      "topics": [
        "dune_trait_env"
      ]
    },
    {
      "page": "fCWM_SNC",
      "title": "Calculate community weighted means and species niche centroids for double constrained correspondence analysis",
      "topics": [
        "fCWM_SNC"
      ]
    },
    {
      "page": "fitted.dcca",
      "title": "Fitted values of double-constrained correspondence analysis (dc-CA)",
      "topics": [
        "fitted.dcca"
      ]
    },
    {
      "page": "getPlotdata",
      "title": "Utility function: extracting data from a 'dc_CA' object for plotting a single axis by your own code or 'plot.dcca'.",
      "topics": [
        "getPlotdata"
      ]
    },
    {
      "page": "plot_dcCA_CWM_SNC",
      "title": "Plot the CWMs and SNCs of a single dc-CA axis.",
      "topics": [
        "plot_dcCA_CWM_SNC"
      ]
    },
    {
      "page": "plot_species_scores_bk",
      "title": "Vertical ggplot2 line plot of ordination scores",
      "topics": [
        "plot_species_scores_bk"
      ]
    },
    {
      "page": "plot.dcca",
      "title": "Plot a single dc-CA axis with CWMs, SNCs, trait and environment scores.",
      "topics": [
        "plot.dcca"
      ]
    },
    {
      "page": "predict.dcca",
      "title": "Prediction for double-constrained correspondence analysis (dc-CA)",
      "topics": [
        "predict.dcca"
      ]
    },
    {
      "page": "print.dcca",
      "title": "Print a summary of a dc-CA object.",
      "topics": [
        "print.dcca"
      ]
    },
    {
      "page": "print.wrda",
      "title": "Print a summary of a wrda object",
      "topics": [
        "print.wrda"
      ]
    },
    {
      "page": "scores.dcca",
      "title": "Extract results of a double constrained correspondence analysis (dc-CA)",
      "topics": [
        "scores.dcca"
      ]
    },
    {
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      "title": "Extract results of a weighted redundancy analysis (wrda)",
      "topics": [
        "scores.wrda"
      ]
    },
    {
      "page": "wrda",
      "title": "Performs a weighted redundancy analysis",
      "topics": [
        "wrda"
      ]
    }
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