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eyeprocess_api_version()
object_schema("eye_dataset")
object_schema("eyeprocess_model")
validate_model_object(fit)
upgrade_eye_dataset(old_data)
upgrade_eyeprocess_model(old_fit)Schemas lock required components, identifiers, return-value
expectations, serialization compatibility, error classes, and scientific
safeguards. eyeprocess_deprecation() records replacement
and removal horizons.
spec <- partition_eye_storage(
by = c("participant_id", "session_id", "recording_id"),
format = "parquet",
compression = "zstd",
max_rows = 1000000L
)
store <- write_partitioned_eye_storage(x, "analysis/store", spec)
query_eye_storage(
store,
table = "gaze_samples",
filters = list(participant_id = c("P001", "P002")),
columns = c("participant_id", "recording_id", "time", "x", "y")
)
validate_eye_storage_metadata(store)
detect_corrupt_partitions(store)
storage_transaction_manifest(store)Writes use a staging directory followed by an atomic commit. Every partition has row count, byte count, partition keys, and a fingerprint. CSV and RDS fallbacks preserve functionality when Arrow is unavailable.
external_model_engines()
fit_mirt_adapter(response_matrix, model = 1, purpose = "unidimensional item calibration")
fit_tam_adapter(response_matrix, purpose = "Rasch sensitivity analysis")
fit_brms_adapter(score ~ dwell + (1|participant_id) + (1|item_id), trials, purpose = "Bayesian explanatory model")
fit_lnirt_adapter(list(Y = response_matrix, RT = rt_matrix), purpose = "joint accuracy-RT comparison")Every adapter returns one of fitted,
not_available, or failed. It does not install
packages, select models, or reinterpret outputs automatically.
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.