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SEIR()
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Model Caller |
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SEIR_M0()
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Run SEIR model for Model 0 |
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SEIR_M1()
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Run SEIR model for Model 1 |
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SEIR_M2()
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Run SEIR model for Model 2 |
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add.to.hist.table()
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Add Rows to the Fit Re Table |
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bind.to.intervention()
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Add Rows to Intervention Table |
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create.beta.vec()
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Creates Vector of Beta Values |
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create.cases.df()
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Create Cases Dataframe |
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create.graph()
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Graphing Helper |
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create.hosp.df()
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Create Hospitalization Dataframe |
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create.res.df()
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Create Hospital Resource Dataframe |
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createTransition()
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Generate transition matrix |
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default_params()
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Returns to Parameter UI elements for the Shiny App |
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find.curr.estimates()
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Find 'Current' Information |
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findBestRe()
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Find Best Fit for Re |
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getBetaFromDoubling()
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Get Beta from Doubling Time |
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getBetaFromRe()
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Get Beta from Re |
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parameters_page()
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Returns to Parameter UI elements for the Shiny App |
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process_df_download()
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General process dataframe to download |
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process_df_download_M0()
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Process dataframe to download for Model 0 |
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process_df_download_M1()
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Process dataframe to download for Model 1 |
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process_df_download_M2()
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Process dataframe to download for Model 2 |
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process_params_download()
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General process parameters for download |
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process_params_download_M0()
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Process parameters for export for Model 0 |
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process_params_download_M1()
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Process parameters for export for Model 0
TODO: If we end up keeping model 1... |
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process_params_download_M2()
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Process parameters for export for Model 2 |
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re_estimate_plot()
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Plot for R_e estimate |
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roundNonDateCols()
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Round Non-Date Columns |
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runMarkov()
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Run Markov Simulation |
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start.exp.default
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Server part |
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start_app()
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Enable bookmarking mode, using values from the URL, if present. |