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  "Title": "Translate Models from System Dynamics Software into 'R'",
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  "Description": "The goal of 'readsdr' is to bridge the design capabilities\nfrom specialised System Dynamics software with the powerful\nnumerical tools offered by 'R' libraries. The package\naccomplishes this goal by parsing 'XMILE' files ('Vensim' and\n'Stella') models into 'R' objects to construct networks (graph\ntheory); 'ODE' functions for 'Stan'; and inputs to simulate via\n'deSolve' as described in Duggan (2016)\n<doi:10.1007/978-3-319-34043-2>.",
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    "extract_timeseries_stock",
    "extract_timeseries_var",
    "inv",
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    "read_xmile",
    "sd_Bayes",
    "sd_conf_intervals",
    "sd_constants",
    "sd_data_generator_fun",
    "sd_fixed_delay",
    "sd_impact_inputs",
    "sd_interpret_estimates",
    "sd_loglik_fun",
    "sd_measurements",
    "sd_net_change",
    "sd_posterior_fun",
    "sd_prior",
    "sd_prior_checks",
    "sd_pulse_s",
    "sd_pulse_train",
    "sd_pulse_v",
    "sd_sensitivity_run",
    "sd_simulate",
    "sd_stocks",
    "sd_what_if_from_time",
    "stan_ode_function",
    "timestep",
    "xmile_to_deSolve"
  ],
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      "name": "Maryland",
      "title": "Influenza in Maryland during the 1918 pandemic",
      "object": "Maryland",
      "class": [
        "spec_tbl_df",
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "Date",
        "Baltimore",
        "Cumberland",
        "Lonaconing",
        "Frederick",
        "Salisbury"
      ],
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      "table": true,
      "tojson": true
    }
  ],
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    {
      "page": "create_stan_function",
      "title": "Create a Stan's ODE function from an XMILE file",
      "topics": [
        "create_stan_function"
      ]
    },
    {
      "page": "expit",
      "title": "Expit transformation",
      "topics": [
        "expit"
      ]
    },
    {
      "page": "extract_timeseries_stock",
      "title": "Extract the values over time of a stock from a Stan fit",
      "topics": [
        "extract_timeseries_stock"
      ]
    },
    {
      "page": "extract_timeseries_var",
      "title": "Extract the values over time of a variable from a Stan fit",
      "topics": [
        "extract_timeseries_var"
      ]
    },
    {
      "page": "inv",
      "title": "Inverse of a number",
      "topics": [
        "inv"
      ]
    },
    {
      "page": "logit",
      "title": "Logit transformation",
      "topics": [
        "logit"
      ]
    },
    {
      "page": "Maryland",
      "title": "Influenza in Maryland during the 1918 pandemic",
      "topics": [
        "Maryland"
      ]
    },
    {
      "page": "read_xmile",
      "title": "Read an XMILE file into R",
      "topics": [
        "read_xmile"
      ]
    },
    {
      "page": "sd_Bayes",
      "title": "Create Stan file for Bayesian inference",
      "topics": [
        "sd_Bayes"
      ]
    },
    {
      "page": "sd_conf_intervals",
      "title": "Calculate confidence intervals",
      "topics": [
        "sd_conf_intervals"
      ]
    },
    {
      "page": "sd_constants",
      "title": "Summarise the information of a model's constants in a data frame",
      "topics": [
        "sd_constants"
      ]
    },
    {
      "page": "sd_data_generator_fun",
      "title": "Function factory for SBC",
      "topics": [
        "sd_data_generator_fun"
      ]
    },
    {
      "page": "sd_fixed_delay",
      "title": "Fixed delay",
      "topics": [
        "sd_fixed_delay"
      ]
    },
    {
      "page": "sd_impact_inputs",
      "title": "Construct inputs for performing structural analysis via the impact method",
      "topics": [
        "sd_impact_inputs"
      ]
    },
    {
      "page": "sd_interpret_estimates",
      "title": "Interpret estimates",
      "topics": [
        "sd_interpret_estimates"
      ]
    },
    {
      "page": "sd_loglik_fun",
      "title": "Generate a log-likelihood function for an SD model",
      "topics": [
        "sd_loglik_fun"
      ]
    },
    {
      "page": "sd_measurements",
      "title": "Generate measurements",
      "topics": [
        "sd_measurements"
      ]
    },
    {
      "page": "sd_net_change",
      "title": "Estimate the net change of a stock in discrete times",
      "topics": [
        "sd_net_change"
      ]
    },
    {
      "page": "sd_posterior_fun",
      "title": "Posterior function",
      "topics": [
        "sd_posterior_fun"
      ]
    },
    {
      "page": "sd_prior",
      "title": "Specify priors for the estimated parameters.",
      "topics": [
        "sd_prior"
      ]
    },
    {
      "page": "sd_prior_checks",
      "title": "Prior predictive checks",
      "topics": [
        "sd_prior_checks"
      ]
    },
    {
      "page": "sd_pulse_s",
      "title": "Replicate the behaviour of the PULSE function from Stella",
      "topics": [
        "sd_pulse_s"
      ]
    },
    {
      "page": "sd_pulse_train",
      "title": "PULSE TRAIN",
      "topics": [
        "sd_pulse_train"
      ]
    },
    {
      "page": "sd_pulse_v",
      "title": "Replicate the behaviour of the PULSE function from Vensim",
      "topics": [
        "sd_pulse_v"
      ]
    },
    {
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      "topics": [
        "sd_sensitivity_run"
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    },
    {
      "page": "sd_simulate",
      "title": "Simulate a System Dynamics model",
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        "sd_simulate"
      ]
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      "page": "xmile_to_deSolve",
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