Package: silentema 1.0.0

silentema: Dynamic Missingness Graphs and Sensitivity Analysis for EMA Data

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

Authors:Hsiu-Ting Yu [aut, cre, cph]