Dynamic process-DIF and fairness drift

Dynamic process-DIF and fairness drift. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

fit_process_dif(x, response, process, group, item, ability = NULL)
monitor_dif_drift(x, time, group, metrics, item = NULL)
decompose_dif_evidence(psychometric, process = NULL, design_features = NULL)
audit_fairness_transportability(x, context = "device", effect = "process_dif",
  item = "item_id")
plot_group_icc_process_overlay(x, ...)
plot_process_dif_forest(x, ...)
plot_dif_drift_heatmap(x, ...)
plot_fairness_transport_matrix(x, ...)
plot_item_group_process_curves(x, ...)

Arguments

x

Input object or data structure appropriate for the selected analysis.

response

Argument controlling `response`; see the function usage and returned audit metadata.

process

Argument controlling `process`; see the function usage and returned audit metadata.

group

Argument controlling `group`; see the function usage and returned audit metadata.

item

Argument controlling `item`; see the function usage and returned audit metadata.

ability

Argument controlling `ability`; see the function usage and returned audit metadata.

time

Argument controlling `time`; see the function usage and returned audit metadata.

metrics

Argument controlling `metrics`; see the function usage and returned audit metadata.

psychometric

Argument controlling `psychometric`; see the function usage and returned audit metadata.

design_features

Argument controlling `design_features`; see the function usage and returned audit metadata.

context

Argument controlling `context`; see the function usage and returned audit metadata.

effect

Argument controlling `effect`; see the function usage and returned audit metadata.

...

Additional arguments passed to the underlying method or plotting function.

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().