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Update forecast-framework.qmd
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forecast-framework.qmd

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@@ -235,10 +235,7 @@ eng <- linear_reg()
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wf <- epi_workflow(r, eng, f) %>% fit(jhu)
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preds <- predict(wf, latest)
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```
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All that really differs from the `arx_forecaster()` is the `recipe`, the
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test data, and the engine. The `frosting` is identical, as is the fitting
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and predicting procedure.
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The code for `arx_forecaster()` simply generalizes this, passing along arguments as needed.
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```{r}
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preds

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