{
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  "Title": "Scalable Joint Species Distribution Modeling",
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  "Authors@R": "c(person(given = \"Maximilian\",\nfamily = \"Pichler\",\nrole = c(\"aut\", \"cre\"),\nemail = \"maximilian.pichler@biologie.uni-regensburg.de\",\ncomment = c(ORCID = \"0000-0003-2252-8327\")),\nperson(given = \"Florian\",\nfamily = \"Hartig\",\nrole = \"aut\",\nemail = \"florian.hartig@biologie.uni-regensburg.de\",\ncomment = c(ORCID = \"0000-0002-6255-9059\")),\nperson(given = \"Wang\",\nfamily = \"Cai\",\nrole = \"ctb\",\nemail = \"caiwang0503@163.com\"))",
  "Description": "A scalable and fast method for estimating joint Species\nDistribution Models (jSDMs) for big community data, including\neDNA data. The package estimates a full (i.e. non-latent) jSDM\nwith different response distributions (including the\ntraditional multivariate probit model). The package allows to\nperform variation partitioning (VP) / ANOVA on the fitted\nmodels to separate the contribution of environmental, spatial,\nand biotic associations. In addition, the total R-squared can\nbe further partitioned per species and site to reveal the\ninternal metacommunity structure, see Leibold et al.,\n<doi:10.1111/oik.08618>. The internal structure can then be\nregressed against environmental and spatial distinctiveness,\nrichness, and traits to analyze metacommunity assembly\nprocesses.  The package includes support for accounting for\nspatial autocorrelation and the option to fit responses using\ndeep neural networks instead of a standard linear predictor. As\ndescribed in Pichler & Hartig (2021)\n<doi:10.1111/2041-210X.13687>, scalability is achieved by using\na Monte Carlo approximation of the joint likelihood implemented\nvia 'PyTorch' and 'reticulate', which can be run on CPUs or\nGPUs.",
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  "Repository": "https://theoreticalecology.r-universe.dev",
  "Date/Publication": "2026-04-21 11:22:40 UTC",
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  "Author": "Maximilian Pichler [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2252-8327>),\nFlorian Hartig [aut] (ORCID: <https://orcid.org/0000-0002-6255-9059>),\nWang Cai [ctb]",
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    "sjSDM.tune",
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      "title": "butterflies",
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      "table": false,
      "tojson": true
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      "class": [
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      "table": false,
      "tojson": true
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      "page": "AccSGD",
      "title": "AccSGD",
      "topics": [
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      "title": "AdaBound",
      "topics": [
        "AdaBound"
      ]
    },
    {
      "page": "Adamax",
      "title": "Adamax",
      "topics": [
        "Adamax"
      ]
    },
    {
      "page": "anova.sjSDM",
      "title": "Anova / Variation partitioning",
      "topics": [
        "anova.sjSDM"
      ]
    },
    {
      "page": "bioticStruct",
      "title": "biotic structure",
      "topics": [
        "bioticStruct"
      ]
    },
    {
      "page": "butterflies",
      "title": "butterflies",
      "topics": [
        "butterflies"
      ]
    },
    {
      "page": "check_module",
      "title": "check module",
      "topics": [
        "check_module"
      ]
    },
    {
      "page": "checkModel",
      "title": "check model check model and rebuild if necessary",
      "topics": [
        "checkModel"
      ]
    },
    {
      "page": "coef.sjSDM",
      "title": "Return coefficients from a fitted sjSDM model",
      "topics": [
        "coef.sjSDM"
      ]
    },
    {
      "page": "DiffGrad",
      "title": "DiffGrad",
      "topics": [
        "DiffGrad"
      ]
    },
    {
      "page": "DNN",
      "title": "Non-linear model (deep neural network) of environmental responses",
      "topics": [
        "DNN"
      ]
    },
    {
      "page": "eucalypts",
      "title": "eucalypts",
      "topics": [
        "eucalypts"
      ]
    },
    {
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      "title": "Generate spatial eigenvectors",
      "topics": [
        "generateSpatialEV"
      ]
    },
    {
      "page": "getCor",
      "title": "getCor",
      "topics": [
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        "getCor.sjSDM"
      ]
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      "page": "getCov",
      "title": "getCov",
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        "getCov.sjSDM"
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      "title": "getImportance",
      "topics": [
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      ]
    },
    {
      "page": "getSe",
      "title": "Post hoc calculation of standard errors",
      "topics": [
        "getSe"
      ]
    },
    {
      "page": "getWeights",
      "title": "Get weights",
      "topics": [
        "getWeights",
        "getWeights.sjSDM"
      ]
    },
    {
      "page": "importance",
      "title": "Importance of environmental, spatial and association components",
      "topics": [
        "importance"
      ]
    },
    {
      "page": "install_diagnostic",
      "title": "install diagnostic",
      "topics": [
        "install_diagnostic"
      ]
    },
    {
      "page": "install_sjSDM",
      "title": "Install sjSDM and its dependencies",
      "topics": [
        "install_sjSDM"
      ]
    },
    {
      "page": "installation_help",
      "title": "Installation help",
      "topics": [
        "sjSDM-package",
        "installation_help"
      ]
    },
    {
      "page": "internalStructure",
      "title": "Plot internal metacommunity structure",
      "topics": [
        "internalStructure"
      ]
    },
    {
      "page": "is_torch_available",
      "title": "is_torch_available",
      "topics": [
        "is_torch_available"
      ]
    },
    {
      "page": "linear",
      "title": "Linear model of environmental response",
      "topics": [
        "linear"
      ]
    },
    {
      "page": "logLik.sjSDM",
      "title": "Extract negative-log-Likelihood from a fitted sjSDM model",
      "topics": [
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      ]
    },
    {
      "page": "madgrad",
      "title": "madgrad",
      "topics": [
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      ]
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      "page": "new_image",
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    },
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      "title": "Coefficients plot",
      "topics": [
        "plot.sjSDM"
      ]
    },
    {
      "page": "plot.sjSDM_cv",
      "title": "Plot elastic net tuning",
      "topics": [
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    },
    {
      "page": "plot.sjSDM.DNN",
      "title": "Training history",
      "topics": [
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      ]
    },
    {
      "page": "plot.sjSDManova",
      "title": "Plot anova results",
      "topics": [
        "plot.sjSDManova"
      ]
    },
    {
      "page": "plot.sjSDMimportance",
      "title": "Plot importance",
      "topics": [
        "plot.sjSDMimportance"
      ]
    },
    {
      "page": "plot.sjSDMinternalStructure",
      "title": "Plot internal structure",
      "topics": [
        "plot.sjSDMinternalStructure"
      ]
    },
    {
      "page": "plotAssemblyEffects",
      "title": "Plot predictors of assembly processes",
      "topics": [
        "plotAssemblyEffects"
      ]
    },
    {
      "page": "plotsjSDMcoef",
      "title": "Internal coefficients plot",
      "topics": [
        "plotsjSDMcoef"
      ]
    },
    {
      "page": "predict.sjSDM",
      "title": "Predict from a fitted sjSDM model",
      "topics": [
        "predict.sjSDM"
      ]
    },
    {
      "page": "print.bioticStruct",
      "title": "Print a bioticStruct object",
      "topics": [
        "print.bioticStruct"
      ]
    },
    {
      "page": "print.DNN",
      "title": "Print a DNN object",
      "topics": [
        "print.DNN"
      ]
    },
    {
      "page": "print.linear",
      "title": "Print a linear object",
      "topics": [
        "print.linear"
      ]
    },
    {
      "page": "print.sjSDM",
      "title": "Print a fitted sjSDM model",
      "topics": [
        "print.sjSDM"
      ]
    },
    {
      "page": "print.sjSDM_cv",
      "title": "Print a fitted sjSDM_cv model",
      "topics": [
        "print.sjSDM_cv"
      ]
    },
    {
      "page": "print.sjSDManova",
      "title": "Print sjSDM anova object",
      "topics": [
        "print.sjSDManova"
      ]
    },
    {
      "page": "print.sjSDMimportance",
      "title": "Print importance",
      "topics": [
        "print.sjSDMimportance"
      ]
    },
    {
      "page": "print.sjSDMinternalStructure",
      "title": "Print internal structure object",
      "topics": [
        "print.sjSDMinternalStructure"
      ]
    },
    {
      "page": "residuals.sjSDM",
      "title": "Residuals for a sjSDM model",
      "topics": [
        "residuals.sjSDM"
      ]
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      "page": "RMSprop",
      "title": "RMSprop",
      "topics": [
        "RMSprop"
      ]
    },
    {
      "page": "Rsquared",
      "title": "R-squared",
      "topics": [
        "Rsquared"
      ]
    },
    {
      "page": "setWeights",
      "title": "Set weights",
      "topics": [
        "setWeights",
        "setWeights.sjSDM"
      ]
    },
    {
      "page": "SGD",
      "title": "SGD",
      "topics": [
        "SGD"
      ]
    },
    {
      "page": "simulate_SDM",
      "title": "Simulate joint Species Distribution Models",
      "topics": [
        "simulate_SDM"
      ]
    },
    {
      "page": "simulate.sjSDM",
      "title": "Generates simulations from sjSDM model",
      "topics": [
        "simulate.sjSDM"
      ]
    },
    {
      "page": "sjSDM",
      "title": "Fitting scalable joint Species Distribution Models (sjSDM)",
      "topics": [
        "sjSDM",
        "sjSDM.tune"
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      "topics": [
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      "page": "sjSDMControl",
      "title": "sjSDM control object",
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        "sjSDMControl"
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      "title": "Return summary of a fitted sjSDM model",
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        "summary.sjSDM"
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    },
    {
      "page": "summary.sjSDM_cv",
      "title": "Return summary of a fitted sjSDM_cv model",
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        "summary.sjSDM_cv"
      ]
    },
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      "page": "summary.sjSDManova",
      "title": "Summary table of sjSDM anova",
      "topics": [
        "summary.sjSDManova"
      ]
    },
    {
      "page": "update.sjSDM",
      "title": "Update and re-fit a model call",
      "topics": [
        "update.sjSDM"
      ]
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      "title": "sjSDM: Getting started with sjSDM - a scalable joint Species Distribution Model",
      "author": "Maximilian Pichler & Florian Hartig, Theoretical Ecology, University of Regensburg",
      "engine": "knitr::rmarkdown",
      "headings": [
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        "Working with sjSDM",
        "Fit a basic jSDM",
        "Interpreting the estimated model coefficients",
        "Interpreting the environmental component",
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        "Regularization on the spatial model:",
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        "Linux - automatic installation:",
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      "created": "2023-03-30 08:16:52",
      "modified": "2025-09-17 10:54:38",
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