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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: not available
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:

Please use the canonical form https://CRAN.R-project.org/package=silentema to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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