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The interoperability layer does not import raw Gazepoint files, process biometric signals, or perform sequence analysis. Those remain upstream responsibilities.
gp3ml_interop_contracts()
#> source_package
#> 1 gp3tools
#> 2 gpbiometrics
#> 3 gp3sequences
#> 4 study_design
#> 5 custom
#> upstream_responsibility
#> 1 Gazepoint import, validation, gaze/fixation/AOI/transition preparation.
#> 2 EDA/HR/DIAL/IBI preparation and signal-quality summaries.
#> 3 Ordered-sequence validation, encoding, summaries, motifs, transitions.
#> 4 Experimentally assigned labels and prespecified study-design variables.
#> 5 Externally prepared observed, non-sensitive variables.
#> gp3ml_responsibility
#> 1 Role declaration, provenance, leakage-safe splitting/resampling, modelling, evaluation, uncertainty, and reporting.
#> 2 Role declaration, provenance, leakage-safe splitting/resampling, modelling, evaluation, uncertainty, and reporting.
#> 3 Role declaration, provenance, leakage-safe splitting/resampling, modelling, evaluation, uncertainty, and reporting.
#> 4 Role declaration, provenance, leakage-safe splitting/resampling, modelling, evaluation, uncertainty, and reporting.
#> 5 Role declaration, provenance, leakage-safe splitting/resampling, modelling, evaluation, uncertainty, and reporting.
#> duplicates_upstream_preprocessing
#> 1 FALSE
#> 2 FALSE
#> 3 FALSE
#> 4 FALSE
#> 5 FALSEbundle <- simulate_gazepoint_research_handoffs(
n_participants = 12L,
n_stimuli = 3L,
seed = 3201L
)
bundle
#> gp3ml research bundle: 3 sources; outcome=assigned_condition; target=new_participants
gaze_validation <- validate_gazepoint_handoff(bundle$handoffs$gp3tools)
gaze_validation
#> gp3ml handoff validation: pass
#> check status
#> supported_source pass
#> tabular_data pass
#> join_keys_present pass
#> join_keys_complete pass
#> join_keys_unique pass
#> predictors_present pass
#> outcome_present pass
#> data_hash_matches pass
#> detail
#> gp3tools
#> 36 rows x 8 columns
#> participant_id, trial_id, stimulus_id
#> No missing join-key values.
#> Composite join key is unique.
#> valid_gaze_prop, fixation_count, mean_fixation_ms, gaze_dispersion
#> assigned_condition
#> Handoff data are unchanged.
plot(gaze_validation)combined <- combine_gazepoint_handoffs(
bundle$handoffs,
keys = bundle$keys
)
combined
#> gp3ml handoff bundle: 3 sources, 36 joined rows
head(as_gp3ml_data(combined))
#> participant_id trial_id stimulus_id assigned_condition valid_gaze_prop
#> 1 P001 T00001 S01 A 0.8042958
#> 2 P002 T00002 S01 B 0.9861274
#> 3 P003 T00003 S01 A 0.8595829
#> 4 P004 T00004 S01 B 0.9394516
#> 5 P005 T00005 S01 A 0.9499491
#> 6 P006 T00006 S01 B 0.9114933
#> fixation_count mean_fixation_ms gaze_dispersion eda_valid_prop hr_valid_prop
#> 1 5 249.4945 0.3123735 0.8789096 0.9249897
#> 2 9 249.6495 0.3129274 0.9198267 1.0000000
#> 3 5 262.8509 0.2911562 0.9153236 0.9871083
#> 4 9 269.6437 0.1767864 0.9896786 0.9492468
#> 5 9 258.2238 0.3265558 0.9045170 0.9851544
#> 6 7 231.5134 0.2586631 0.9898708 0.9493813
#> ibi_valid_prop sequence_length unique_state_count transition_rate
#> 1 0.9235095 6 2 0.6087000
#> 2 0.8933526 11 2 0.6243513
#> 3 0.8776888 12 6 0.5684036
#> 4 0.9722860 9 3 0.6700565
#> 5 0.9023553 4 6 0.6221709
#> 6 0.9732340 12 3 0.5557973The resulting table is a modelling handoff. It does not imply that
gp3ml performed the upstream preprocessing represented by
those columns.
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.