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  "Title": "Multivariate (Dynamic) Generalized Additive Models",
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  "Date": "2026-01-19",
  "Authors@R": "c(person(\"Nicholas J\", \"Clark\", email = \"nicholas.j.clark1214@gmail.com\", \nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-7131-3301\")),\nperson(\"KANK\", \"Karunarathna\", role = c(\"ctb\"),\ncomment = c(\"ARMA parameterisations and factor models\", ORCID = \"0000-0002-8995-5502\")),\nperson(\"Sarah\", \"Heaps\", role = c(\"ctb\"),\ncomment = c(\"VARMA parameterisations\", ORCID = \"0000-0002-5543-037X\")),\nperson(\"Scott\", \"Pease\", role = c(\"ctb\"),\ncomment = c(\"broom enhancements\", ORCID = \"0009-0006-8977-9285\")),\nperson(\"Matthijs\", \"Hollanders\", role = c(\"ctb\"),\ncomment = c(\"ggplot visualizations\", ORCID = \"0000-0003-0796-1018\")))",
  "Description": "Fit Bayesian Dynamic Generalized Additive Models to\nmultivariate observations. Users can build nonlinear\nState-Space models that can incorporate semiparametric effects\nin observation and process components, using a wide range of\nobservation families. Estimation is performed using Markov\nChain Monte Carlo with Hamiltonian Monte Carlo in the software\n'Stan'. References: Clark & Wells (2023)\n<doi:10.1111/2041-210X.13974>.",
  "URL": "https://github.com/nicholasjclark/mvgam,\nhttps://nicholasjclark.github.io/mvgam/",
  "BugReports": "https://github.com/nicholasjclark/mvgam/issues",
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  "Date/Publication": "2026-02-13 21:17:31 UTC",
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  "Author": "Nicholas J Clark [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-7131-3301>),\nKANK Karunarathna [ctb] (ARMA parameterisations and factor models,\nORCID: <https://orcid.org/0000-0002-8995-5502>),\nSarah Heaps [ctb] (VARMA parameterisations, ORCID:\n<https://orcid.org/0000-0002-5543-037X>),\nScott Pease [ctb] (broom enhancements, ORCID:\n<https://orcid.org/0009-0006-8977-9285>),\nMatthijs Hollanders [ctb] (ggplot visualizations, ORCID:\n<https://orcid.org/0000-0003-0796-1018>)",
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        "add_residuals.mvgam"
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      "topics": [
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      "title": "Augment an 'mvgam' object's data",
      "concept": [
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        "stancode.mvgam",
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        "print.mvgam_conditional_effects"
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      "title": "Combine forecasts from 'mvgam' models into evenly weighted ensembles",
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        "ensemble.mvgam_forecast"
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        "evaluate_mvgams",
        "eval_mvgam",
        "roll_eval_mvgam"
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      "title": "Calculate latent VAR forecast error variance decompositions",
      "topics": [
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        "fevd.mvgam"
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      "page": "fitted.mvgam",
      "title": "Expected values of the posterior predictive distribution for 'mvgam' objects",
      "topics": [
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      "title": "Extract or compute hindcasts and forecasts for a fitted 'mvgam' object",
      "topics": [
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      "title": "Extract formulae from 'mvgam' objects",
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        "formula.mvgam_prefit"
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      "topics": [
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        "eval_smooth.moi.smooth",
        "eval_smoothDothilbertDotsmooth",
        "eval_smoothDotmodDotsmooth",
        "eval_smoothDotmoiDotsmooth",
        "gratia_mvgam_enhancements"
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      "page": "hindcast.mvgam",
      "title": "Extract hindcasts for a fitted 'mvgam' object",
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        "hindcast.mvgam"
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    {
      "page": "how_to_cite.mvgam",
      "title": "Generate a methods description for 'mvgam' models",
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        "how_to_cite.mvgam"
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      "page": "irf.mvgam",
      "title": "Calculate latent VAR impulse response functions",
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        "irf.mvgam"
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      "page": "jsdgam",
      "title": "Fit Joint Species Distribution Models in 'mvgam'",
      "topics": [
        "jsdgam"
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    {
      "page": "lfo_cv.mvgam",
      "title": "Approximate leave-future-out cross-validation of fitted 'mvgam' objects",
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        "lfo_cv.mvgam"
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    {
      "page": "logLik.mvgam",
      "title": "Compute pointwise Log-Likelihoods from fitted 'mvgam' objects",
      "topics": [
        "logLik.mvgam"
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    {
      "page": "loo.mvgam",
      "title": "LOO information criteria for 'mvgam' models",
      "topics": [
        "loo.mvgam",
        "loo_compare.mvgam"
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    },
    {
      "page": "lv_correlations",
      "title": "Calculate trend correlations based on latent factor loadings for 'mvgam' models",
      "topics": [
        "lv_correlations"
      ]
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    {
      "page": "mcmc_plot.mvgam",
      "title": "MCMC plots of 'mvgam' parameters, as implemented in 'bayesplot'",
      "topics": [
        "mcmc_plot.mvgam"
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    {
      "page": "model.frame.mvgam",
      "title": "Extract model.frame from a fitted 'mvgam' object",
      "topics": [
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        "model.frame.mvgam_prefit"
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    {
      "page": "monotonic",
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      "page": "mvgam",
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        "mvgam"
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      "page": "mvgam_diagnostics",
      "title": "Extract diagnostic quantities of 'mvgam' models",
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        "neff_ratio.mvgam",
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        "nuts_params.mvgam",
        "rhat",
        "rhat.mvgam"
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    {
      "page": "mvgam_draws",
      "title": "Extract posterior draws from fitted 'mvgam' objects",
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        "as.data.frame.mvgam",
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    {
      "page": "mvgam_families",
      "title": "Supported 'mvgam' families",
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        "betar",
        "beta_binomial",
        "lognormal",
        "mvgam_families",
        "nb",
        "nmix",
        "student",
        "student_t",
        "tweedie"
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      "page": "mvgam_forecast-class",
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    {
      "page": "mvgam_formulae",
      "title": "Details of formula specifications in 'mvgam' models",
      "topics": [
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      "page": "mvgam_irf-class",
      "title": "'mvgam_irf' object description",
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        "mvgam_irf-class"
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    {
      "page": "mvgam_marginaleffects",
      "title": "Helper functions for 'marginaleffects' calculations in 'mvgam' models",
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      "page": "mvgam_residcor-class",
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    {
      "page": "mvgam_trends",
      "title": "Supported latent trend models in 'mvgam'",
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      "title": "Fitted 'mvgam' object description",
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    {
      "page": "ordinate.jsdgam",
      "title": "Latent variable ordination plots from jsdgam objects",
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        "ordinate.jsdgam"
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    {
      "page": "pairs.mvgam",
      "title": "Create a matrix of output plots from a 'mvgam' object",
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      "page": "plot_mvgam_factors",
      "title": "Latent factor summaries for a fitted 'mvgam' object",
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    {
      "page": "plot_mvgam_forecasts",
      "title": "Plot posterior forecast predictions from 'mvgam' models",
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        "plot_mvgam_forecasts"
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      "page": "plot_mvgam_pterms",
      "title": "Plot parametric term partial effects for 'mvgam' models",
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      "page": "plot_mvgam_randomeffects",
      "title": "Plot random effect terms from 'mvgam' models",
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      "page": "plot_mvgam_resids",
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      "page": "plot_mvgam_smooth",
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      "page": "plot_mvgam_uncertainty",
      "title": "Plot forecast uncertainty contributions from 'mvgam' models",
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      "page": "plot.mvgam_fevd",
      "title": "Plot forecast error variance decompositions from an 'mvgam_fevd' object",
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      "page": "plot.mvgam_irf",
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      "page": "plot.mvgam_lfo",
      "title": "Plot Pareto-k and ELPD values from a 'mvgam_lfo' object",
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    {
      "page": "plot.mvgam_residcor",
      "title": "Plot residual correlations based on latent factors",
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    {
      "page": "portal_data",
      "title": "Portal Project rodent capture survey data",
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      "page": "posterior_epred.mvgam",
      "title": "Draws from the expected value of the posterior predictive distribution for 'mvgam' objects",
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      "page": "posterior_linpred.mvgam",
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      "page": "posterior_predict.mvgam",
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      "title": "Plot conditional posterior predictive checks from 'mvgam' models",
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      "title": "Predict from a fitted 'mvgam' model",
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      "title": "Print a fitted 'mvgam' object",
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      "page": "print.mvgam_summary",
      "title": "Print method for mvgam_summary objects",
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    {
      "page": "piecewise_trends",
      "title": "Specify piecewise linear or logistic trends in 'mvgam' models",
      "topics": [
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    },
    {
      "page": "residual_cor.jsdgam",
      "title": "Extract residual correlations based on latent factors",
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    {
      "page": "residuals.mvgam",
      "title": "Posterior draws of residuals from 'mvgam' models",
      "topics": [
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    {
      "page": "RW",
      "title": "Specify autoregressive dynamic processes in 'mvgam'",
      "topics": [
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    {
      "page": "score.mvgam_forecast",
      "title": "Compute probabilistic forecast scores for 'mvgam' models",
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    {
      "page": "series_to_mvgam",
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      "title": "Calculate measures of latent VAR community stability",
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      "title": "Forecasting and forecast evaluation in mvgam",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Simulating discrete time series",
        "Modelling dynamics with splines",
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        "Forecasting with the forecast() function",
        "Forecasting with newdata in mvgam()",
        "Scoring forecast distributions",
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      "filename": "data_in_mvgam.html",
      "title": "Formatting data for use in mvgam",
      "author": "Nicholas J Clark",
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        "Irregular sampling intervals?",
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      "title": "Overview of the mvgam package",
      "author": "Nicholas J Clark",
      "engine": "knitr::rmarkdown",
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