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Updated documentation. Especially new recommendations for K in ppc_pit_ecdf*() and mcmc_rank_ecdf().
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R/helpers-ppc.R

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@@ -416,12 +416,12 @@ adjust_gamma_simulate <- function(N, L, K, prob, M) {
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#' simultaneous coverage of the ECDF traces.
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#' @noRd
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interpolate_gamma <- function(N, K, prob, L) {
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# Find the precomputed values ueful for the interpolation task.
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# Find the precomputed values useful for the interpolation task.
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vals <- get_interpolation_values(N, K, L, prob)
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# Largest lower bound and smalles upper bound for N among precomputed values.
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N_lb <- max(vals[vals$N <= N, ]$N)
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N_ub <- min(vals[vals$N >= N, ]$N)
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# Approximate largest lower bound and smalles upper bound for gamma.
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# Approximate largest lower bound and smallest upper bound for gamma.
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log_gamma_lb <- approx(
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x = log(vals[vals$N == N_lb, ]$K),
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y = log(vals[vals$N == N_lb, ]$val),

man-roxygen/args-pit-ecdf.R

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#' @param K An optional integer defining the number of equally spaced evaluation
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#' points for the ECDF. If the submitted PIT values are known to be discrete,
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#' this should be the number of the discrete cases. Defaults to the smaller of
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#' `length(y)` and `ncol(yrep)` when applicable.
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#' points for the ECDF. Reducing K when using `interpolate_adj = FALSE` makes
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#' computing the confidence bands faster. For `ppc_pit_ecdf` and
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#' `ppc_pit_ecdf_grouped`, defaults to `ncol(yrep) + 1`, or `length(pit)` if PIT
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#' values are supplied. For `mcmc_rank_ecdf` defaults to the number of
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#' iterations per chain in `x`.
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#' @param prob The desired simultaneous coverage level of the bands around the
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#' ECDF. A value in (0,1).
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#' @param plot_diff A boolean defining whether to plot the difference between

man/MCMC-traces.Rd

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man/PPC-distributions.Rd

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man/bayesplot-package.Rd

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