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DESCRIPTION

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@@ -25,7 +25,6 @@ Depends:
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parsnip (>= 1.0.0),
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R (>= 3.5.0)
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Imports:
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assertthat,
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cli,
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distributional,
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dplyr,

NAMESPACE

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export(step_training_window)
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export(validate_layer)
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import(distributional)
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import(epiprocess)
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import(parsnip)
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import(recipes)
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import(vctrs)
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importFrom(generics,augment)

R/epipredict-package.R

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#' @importFrom tibble tibble
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#' @importFrom rlang := !!
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#' @importFrom stats poly predict lm residuals quantile
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#' @import epiprocess parsnip
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NULL

vignettes/preprocessing-and-models.Rmd

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The expression of the multinomial regression we will model is as follows:
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$$
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\pi_{j}(x) = \text{Pr}(Y = j|x) = \frac{e^{g_j(x)}}{\sum_{k=0}^2 g_j(x) }
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\pi_{j}(x) = \text{Pr}(Y = j|x) = \frac{e^{g_j(x)}}{1 + \sum_{k=0}^2 g_j(x) }
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$$
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where $j$ is either down (`Y=0`), flat (`Y=1`), or up (`Y=2`),
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$$g_0(x) = 0$$

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