Why add intervals? | Example data | Residual bootstrap confidence bands | Wild bootstrap confidence bands | Compare multiple models | Nonlinear fits | Choosing a method

Overview | Compare models quickly with fit_lin() | Nonlinear fitting with starting values | Weighted nonlinear fitting | Estimate K when the epidemic plateaus below 1 | Grouped fitting with fit_multi() | Practical reporting

Overview | Load the data | Inspect the experimental structure | Visualize treatment means over time | Calculate AUDPC and AUDPS per experimental unit | Fit a model to one observed curve | Fit models to many observed curves at once | Practical guidance | Data source

Overview | Simulate four canonical curve shapes | Send simulated data into the fitting pipeline

What epifitter does | A first epidemic | Fit candidate models | Visualize predictions | Work with multiple epidemics | Next steps

Overview | Build an example curve | Compute AUDPC | Multiple observations at the same time | One summary curve versus one value per replicate | Compute AUDPS | Estimate AUDPC from two observations | Absolute versus relative measures | Practical guidance

When to Use This Workflow | Packages and Data | Inspect the Raw Pattern | Check the Data | Fit Candidate Models | Diagnose Fits | Rank Candidate Models | Plot All Models or Best Models | Customize with Curve Data | Predict and Report Selected Models | Residual Diagnostics

When to Use This Workflow | Packages and Data | Check Fit Readiness | Fit the Model | Review the Fit Object | Plot the Fitted Curves | Predict and Report | Build a Custom Figure | Check Residuals

Goal | Packages | Example Data | Check the Data | Fit Candidate Models | Inspect Fit Quality | Select Models | Plot Curves | Report and Reuse Results | Diagnose Residuals