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  "Description": "Tools for diagnosing and correcting informative\nnonresponse in ecological momentary assessment (EMA) and other\nexperience-sampling designs. Declares the assumed nonresponse\nmechanism as a dynamic missingness graph built from a taxonomy\nof seven motifs, following the graphical missing-data framework\nof Mohan and Pearl (2021) <doi:10.1080/01621459.2021.1874961>;\nchecks by d-separation which within-person and between-person\nestimands of a two-level vector autoregressive model remain\nrecoverable and by which estimator; tests whether skipped\nprompts were informative (the silence test and the sensor-gap\ntest, with cluster-robust inference after Cameron and Miller\n(2015) <doi:10.3368/jhr.50.2.317>); estimates the temporal and\ncontemporaneous networks from answered adjacent prompts with\nthe half-panel jackknife of Dhaene and Jochmans (2015)\n<doi:10.1093/restud/rdv007>, by inverse-probability weighting\non an observed context, and by full-information maximum\nlikelihood with the state-space expectation-maximization (EM)\nalgorithm of Shumway and Stoffer (1982)\n<doi:10.1111/j.1467-9892.1982.tb00349.x>; profiles the\nestimates over a self-censoring sensitivity parameter\n(inverse-probability weighting with a fixed probit selection\nmodel whose intercept is calibrated to the response rate);\ncalibrates that parameter from passive sensors, randomized\nprobes, or the post-skip contrast; computes worst-case bounds\nfor person means in the spirit of Manski (2003)\n<doi:10.1007/b97478>; writes a preregistration-ready\nmissingness declaration; and simulates experience-sampling data\nunder every motif. The methods are described in Yu (2026,\nmanuscript under review); the accompanying materials are\narchived at <https://osf.io/x6d2t/>.",
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