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blocks <- process_feature_blocks(
person_data,
blocks = list(
Psychometric = c("theta_hat", "mean_accuracy"),
Gaze = c("mean_rt_ms", "mean_dwell_ms", "mean_revisits", "mean_aoi_entropy"),
Pupil = c("mean_pupil_peak", "mean_pupil_auc"),
Quality = c("mean_valid_gaze_prop", "mean_valid_pupil_prop")
),
id = "person_id"
)
mfa <- fit_multiblock_process_map(blocks)
multiblock_person_coordinates(mfa)
multiblock_variable_coordinates(mfa)
multiblock_contributions(mfa)
plot(mfa, type = "individuals")
plot(mfa, type = "variables")This is exploratory block-aware mapping, not a replacement for multimodal IRT.
profiles <- fit_process_profile_mixture(
person_data,
variables = c("mean_dwell_ms", "mean_pupil_peak", "mean_aoi_entropy", "mean_revisits"),
k = 3
)
process_profile_summary(profiles)
process_profile_probabilities(profiles)
plot(profiles, type = "profiles")Profile names remain neutral until externally validated.
valid <- audit_process_external_validity(
person_data,
criterion = "independent_task_score",
predictors = c("theta_hat", "mean_pupil_peak", "mean_dwell_ms", "mean_aoi_entropy"),
baseline_predictors = c("age")
)
process_criterion_associations(valid)
incremental_process_validity(valid)
plot(valid, type = "associations")
plot(valid, type = "incremental")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.