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Merge branch 'cdc-baseline' of https://github.com/cmu-delphi/epipredict into cdc-baseline
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.github/workflows/R-CMD-check.yaml

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# Created with usethis + edited to use API key.
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on:
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push:
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branches: [main, master]
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branches: [main, master, v0.0.6]
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pull_request:
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branches: [main, master]
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branches: [main, master, v0.0.6]
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name: R-CMD-check
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R/layer_cdc_flatline_quantiles.R

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#' These samples are spaced evenly on the (0, 1) scale, F_X(x) resulting in
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#' linear interpolation on the X scale. This is achieved with
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#' [stats::quantile()] Type 7 (the default for that function).
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#' @param symmetrize Logical. If `TRUE`, does two things: (i) forces the
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#' @param symmetrize Scalar logical. If `TRUE`, does two things: (i) forces the
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#' "empirical" CDF of residuals to be symmetric by pretending that for every
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#' actually-observed residual X we also observed another residual -X, and (ii)
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#' at each ahead, forces the median simulated value to be equal to the point
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#' simulating the next ahead. This forces any 1-ahead predictive intervals to
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#' be symmetric about the point prediction, and encourages larger aheads to be
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#' more symmetric.
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#' @param nonneg Logical. Force all predictive intervals be non-negative.
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#' @param nonneg Scalar logical. Force all predictive intervals be non-negative.
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#' Because non-negativity is forced _before_ propagating forward, this has
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#' slightly different behaviour than would occur if using [layer_threshold()].
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#' Thresholding at each ahead takes place after any shifting from
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if (symmetrize) {
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r <- c(r, -r)
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}
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samp <- quantile(r, probs = c(0, seq_len(nsim - 1)) / (nsim - 1), na.rm = TRUE)
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samp <- quantile(r, probs = c(0, seq_len(nsim - 1)) / (nsim - 1),
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na.rm = TRUE, names = FALSE)
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res <- list()
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raw <- samp + p

man/layer_cdc_flatline_quantiles.Rd

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