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  "Title": "Bayesian Dynamic Factor Analysis (DFA) with 'Stan'",
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  "Description": "Implements Bayesian dynamic factor analysis with 'Stan'.\nDynamic factor analysis is a dimension reduction tool for\nmultivariate time series. 'bayesdfa' extends conventional\ndynamic factor models in several ways. First, extreme events\nmay be estimated in the latent trend by modeling process error\nwith a student-t distribution. Second, alternative constraints\n(including proportions are allowed). Third, the estimated\ndynamic factors can be analyzed with hidden Markov models to\nevaluate support for latent regimes.",
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    "find_dfa_trends",
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    "find_regimes",
    "find_swans",
    "fit_dfa",
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    "invert_chains",
    "is_converged",
    "loo",
    "plot_fitted",
    "plot_loadings",
    "plot_regime_model",
    "plot_trends",
    "predicted",
    "rotate_trends",
    "sim_dfa",
    "trend_cor"
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      "page": "bayesdfa-package",
      "title": "The 'bayesdfa' package.",
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        "bayesdfa-package",
        "bayesdfa"
      ]
    },
    {
      "page": "dfa_cv",
      "title": "Apply cross validation to DFA model",
      "topics": [
        "dfa_cv"
      ]
    },
    {
      "page": "dfa_fitted",
      "title": "Get the fitted values from a DFA as a data frame",
      "topics": [
        "dfa_fitted"
      ]
    },
    {
      "page": "dfa_loadings",
      "title": "Get the loadings from a DFA as a data frame",
      "topics": [
        "dfa_loadings"
      ]
    },
    {
      "page": "dfa_trends",
      "title": "Get the trends from a DFA as a data frame",
      "topics": [
        "dfa_trends"
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    },
    {
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      "title": "Find the best number of trends according to LOOIC",
      "topics": [
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    },
    {
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      "title": "Find which chains to invert",
      "topics": [
        "find_inverted_chains"
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    },
    {
      "page": "find_regimes",
      "title": "Fit multiple models with differing numbers of regimes to trend data",
      "topics": [
        "find_regimes"
      ]
    },
    {
      "page": "find_swans",
      "title": "Find outlying \"black swan\" jumps in trends",
      "topics": [
        "find_swans"
      ]
    },
    {
      "page": "fit_dfa",
      "title": "Fit a Bayesian DFA",
      "topics": [
        "fit_dfa"
      ]
    },
    {
      "page": "fit_regimes",
      "title": "Fit models with differing numbers of regimes to trend data",
      "topics": [
        "fit_regimes"
      ]
    },
    {
      "page": "hmm_init",
      "title": "Create initial values for the HMM model.",
      "topics": [
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      ]
    },
    {
      "page": "invert_chains",
      "title": "Invert chains",
      "topics": [
        "invert_chains"
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    {
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      "title": "Summarize Rhat convergence statistics across parameters",
      "topics": [
        "is_converged"
      ]
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    {
      "page": "loo",
      "title": "LOO information criteria",
      "topics": [
        "loo",
        "loo.bayesdfa"
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    },
    {
      "page": "plot_fitted",
      "title": "Plot the fitted values from a DFA",
      "topics": [
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      "title": "Plot the loadings from a DFA",
      "topics": [
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    {
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      "title": "Plot the state probabilities from 'find_regimes()'",
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    },
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      "page": "plot_trends",
      "title": "Plot the trends from a DFA",
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    {
      "page": "predicted",
      "title": "Calculate predicted value from DFA object",
      "topics": [
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      "page": "rotate_trends",
      "title": "Rotate the trends from a DFA",
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    {
      "page": "sim_dfa",
      "title": "Simulate from a DFA",
      "topics": [
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      ]
    },
    {
      "page": "trend_cor",
      "title": "Estimate the correlation between a DFA trend and some other timeseries",
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