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/>.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: Rcpp (≥ 1.0.7), stats, graphics, grDevices
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2026-10-08
DOI: 10.32614/CRAN.package.silentema (may not be active yet)
Author: Hsiu-Ting Yu ORCID iD [aut, cre, cph]
Maintainer: Hsiu-Ting Yu <hsiutingyu at gmail.com>
BugReports: https://github.com/hsiutingyu/silentema/issues
License: GPL (≥ 3)
URL: https://github.com/hsiutingyu/silentema, https://hsiutingyu.github.io/silentema/, https://osf.io/x6d2t/
NeedsCompilation: yes
Language: en-US
Citation: silentema citation info
Materials: README, NEWS
CRAN checks: silentema results

Documentation:

Reference manual: silentema.html , silentema.pdf
Vignettes: Dynamic missingness graphs: the motif taxonomy and recoverability (source, R code)
Sensitivity analysis for self-censoring: tilting, break-even values and calibration (source, R code)
Getting started: the silentema workflow (source, R code)
Simulating experience-sampling data and planning a design (source, R code)
Was silence informative? The silence test, the sensor-gap test and the fatigue check (source, R code)

Downloads:

Package source: silentema_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: silentema_1.0.0.zip
macOS binaries: r-release (arm64): silentema_1.0.0.tgz, r-oldrel (arm64): silentema_1.0.0.tgz, r-release (x86_64): silentema_1.0.0.tgz, r-oldrel (x86_64): silentema_1.0.0.tgz

Linking:

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