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Eliminate redundant data processing in mcmc_areas_data() #501

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Eliminate redundant data processing in mcmc_areas_data() #501

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008a9b1
Eliminate redundant data processing in mcmc_areas_data()
utkarshpawade Mar 22, 2026
1d6bd1f
Extract compute_intervals() helper to avoid exposing internal parameter
utkarshpawade Mar 22, 2026
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1 change: 1 addition & 0 deletions NEWS.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# bayesplot (development version)

* Eliminate redundant data processing in `mcmc_areas_data()` by reusing the prepared MCMC array for both interval and density computation.
* Validate equal chain lengths in `validate_df_with_chain()`, reject missing
chain labels, and renumber data-frame chain labels internally when converting
to arrays.
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23 changes: 15 additions & 8 deletions R/mcmc-intervals.R
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Expand Up @@ -599,13 +599,20 @@ mcmc_intervals_data <- function(x,
prob <- probs[1]
prob_outer <- probs[2]

x <- prepare_mcmc_array(x, pars, regex_pars, transformations)
x <- merge_chains(x)

data_long <- melt_mcmc(x) %>%
data_long <- melt_mcmc(
merge_chains(prepare_mcmc_array(x, pars, regex_pars, transformations))
) %>%
dplyr::as_tibble() %>%
rlang::set_names(tolower)

compute_intervals(data_long, prob, prob_outer, point_est, rhat)
}

# Internal helper shared by mcmc_intervals_data() and mcmc_areas_data()
compute_intervals <- function(data_long, prob, prob_outer,
point_est = c("median", "mean", "none"),
rhat = numeric()) {

probs <- c(0.5 - prob_outer / 2,
0.5 - prob / 2,
0.5 + prob / 2,
Expand Down Expand Up @@ -691,17 +698,17 @@ mcmc_areas_data <- function(x,
point_est <- match.arg(point_est)
temp_point_est <- if (point_est == "none") "median" else point_est

intervals <- mcmc_intervals_data(x, pars, regex_pars, transformations,
prob = probs[1], prob_outer = probs[2],
point_est = temp_point_est, rhat = rhat)

x <- prepare_mcmc_array(x, pars, regex_pars, transformations)
x <- merge_chains(x)

data_long <- melt_mcmc(x) %>%
dplyr::as_tibble() %>%
rlang::set_names(tolower)

intervals <- compute_intervals(data_long, prob = probs[1],
prob_outer = probs[2],
point_est = temp_point_est, rhat = rhat)

# Compute the density intervals
data_inner <- data_long %>%
compute_column_density(
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