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The estimand layer operates on posterior prediction draws. For documentation and tests, a deterministic draw matrix can be used without compiling Stan.
grid <- expand.grid(
.event_time = seq(0, 1, length.out = 21),
.condition = factor(c("control", "treatment")),
KEEP.OUT.ATTRS = FALSE
)
set.seed(2026)
mu <- ifelse(grid$.condition == "treatment", 0.15, 0) +
0.25 * sin(pi * grid$.event_time)
draws <- matrix(
rnorm(300 * nrow(grid), rep(mu, each = 300), 0.06),
nrow = 300
)
pred <- as_pupil_prediction_draws(draws, grid, "millimetres")trajectory <- estimate_pupil_trajectory(pred, probability = 0.95)
head(pupil_trajectory_table(trajectory))
#> .event_time .condition estimate median lower upper
#> 1 0.00 control 0.002499136 -0.0003379517 -0.109771080 0.1237874
#> 2 0.05 control 0.043415282 0.0501940460 -0.081746697 0.1660529
#> 3 0.10 control 0.074564976 0.0717499708 -0.031731909 0.1971629
#> 4 0.15 control 0.114372026 0.1152661166 -0.002348101 0.2363749
#> 5 0.20 control 0.142101058 0.1402868252 0.028388224 0.2545414
#> 6 0.25 control 0.180003590 0.1786603138 0.068083824 0.2910737Pointwise intervals describe uncertainty at each grid value. A finite-grid simultaneous band can be requested explicitly; it is qualified as a grid-based posterior band rather than a universal continuous-time guarantee.
contrast <- pupil_condition_contrast(
pred,
contrast = c("treatment", "control"),
threshold = 0.10
)
window <- estimate_pupil_window(pred, window = c(0.3, 0.8))
auc <- estimate_pupil_auc(pred, window = c(0.3, 0.8))
peak <- estimate_pupil_peak(pred, window = c(0.3, 0.8))
latency <- estimate_pupil_peak_latency(pred, window = c(0.3, 0.8))
head(as.data.frame(contrast))
#> .event_time contrast estimate median lower upper
#> 22 0.00 treatment - control 0.1512366 0.1480115 -0.002193476 0.3195893
#> 23 0.05 treatment - control 0.1456269 0.1386465 -0.002451938 0.3172826
#> 24 0.10 treatment - control 0.1554272 0.1563937 -0.017678839 0.3005029
#> 25 0.15 treatment - control 0.1461007 0.1519917 -0.018632237 0.3023661
#> 26 0.20 treatment - control 0.1611109 0.1606923 -0.011704219 0.3428709
#> 27 0.25 treatment - control 0.1463584 0.1494409 -0.019417686 0.3219812
#> threshold probability_gt_threshold
#> 22 0.1 0.7533333
#> 23 0.1 0.7066667
#> 24 0.1 0.7500000
#> 25 0.1 0.7033333
#> 26 0.1 0.7266667
#> 27 0.1 0.7200000
as.data.frame(window)
#> .condition estimand estimate median lower upper window_start
#> 1 control window_mean 0.2164691 0.2171261 0.1804861 0.2549529 0.3
#> 2 treatment window_mean 0.3680499 0.3680008 0.3300174 0.4051905 0.3
#> window_end
#> 1 0.8
#> 2 0.8
as.data.frame(auc)
#> .condition estimand estimate median lower upper window_start
#> 1 control auc 0.1105039 0.1107491 0.09204704 0.1303858 0.3
#> 2 treatment auc 0.1861384 0.1863730 0.16620597 0.2057661 0.3
#> window_end
#> 1 0.8
#> 2 0.8
as.data.frame(peak)
#> .condition estimand estimate median lower upper window_start
#> 1 control peak 0.3231959 0.3239621 0.2594610 0.3998760 0.3
#> 2 treatment peak 0.4753597 0.4671045 0.4115866 0.5624902 0.3
#> window_end
#> 1 0.8
#> 2 0.8
as.data.frame(latency)
#> .condition estimand estimate median lower upper window_start
#> 1 control peak_latency 0.5131667 0.5 0.3420833 0.75 0.3
#> 2 treatment peak_latency 0.4960000 0.5 0.3000000 0.70 0.3
#> window_end
#> 1 0.8
#> 2 0.8Windows are supplied by the analyst. The package does not search across time for the most favourable interval and relabel it confirmatory. Peak and peak-latency summaries propagate posterior-draw uncertainty within the declared evaluation grid.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.