Calibrating Generated Items Before Pretesting with Predicted Priors
Overview
Predicts Rasch item difficulty from item features (text embeddings from any model, template family, content metadata) by ridge regression (Hoerl and Kennard, 1970, doi:10.1080/00401706.1970.10488634) with honest out-of-sample uncertainty, uses the predictions as robust informative priors to calibrate new items from small pretest samples, in the spirit of using collateral information in calibration (Mislevy, Sheehan and Wingersky, 1993, doi:10.1111/j.1745-3984.1993.tb00422.x), plans the pretest sample size needed for a target precision, and diagnoses item families whose predictions cannot be trusted.
cs_check()now accounts for prediction error shared by all items of a family (the unseen family's unknown effect, or the estimation error of a seen family's effect).predict()returns this assd_shared, estimated from the cross-validation and leave-one-family-out residuals, andcs_calibrate()carries it asprior_sd_shared. Previously a family unseen in training was falsely flagged 3-7% of the time at alpha = 0.01; it is now flagged at about the nominal rate. Seen families, detection of drifted families and calibration accuracy are unchanged. Calibrations without the new column behave exactly as before.
From NEWS.md in the release
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Package metadata
- First published
- 2026-10-07
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1
- Download size
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