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A process feature should not be treated as an individual-difference measure until its dependability across items, sessions, and devices is quantified.
gstudy <- fit_process_gstudy(
process_long,
metric = "pupil_auc",
facets = c("person", "item", "session", "device")
)
process_variance_components(gstudy)
plot_variance_components(gstudy)
dstudy <- design_process_dstudy(
gstudy,
items = seq(5, 40, 5),
sessions = 1:4,
devices = 1:2
)
plot_dependability_surface(dstudy)
reliability <- audit_process_reliability(
process_long,
metrics = c("dwell_ms", "pupil_auc", "aoi_entropy"),
method = "icc"
)
plot_reliability_by_metric(reliability)Vendor-neutral import does not imply metric equivalence. Paired cross-device data can be linked and audited against a declared equivalence margin.
link <- fit_device_linking(
paired_device_data,
metric = "pupil_auc",
reference_device = "laboratory_reference",
id_cols = c("person_id", "trial_id")
)
plot_device_agreement(link)
plot_device_bias_by_magnitude(link)
plot_device_transfer_curve(link)
equivalence <- audit_device_equivalence(link, equivalence_margin = 0.05)
plot_device_equivalence_intervals(equivalence)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.