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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Dynamic Missingness Graphs and Sensitivity Analysis for EMA Data</dc:title>
  <dc:title>R package silentema version 1.0.0</dc:title>
  <dc:description>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) &lt;doi:10.1080/01621459.2021.1874961&gt;; 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) &lt;doi:10.3368/jhr.50.2.317&gt;); estimates the temporal and
    contemporaneous networks from answered adjacent prompts with the half-panel
    jackknife of Dhaene and Jochmans (2015) &lt;doi:10.1093/restud/rdv007&gt;, 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)
    &lt;doi:10.1111/j.1467-9892.1982.tb00349.x&gt;;
    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)
    &lt;doi:10.1007/b97478&gt;; 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 &lt;https://osf.io/x6d2t/&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 4.1.0)</dc:relation>
  <dc:relation>Imports: Rcpp (&gt;= 1.0.7), stats, graphics, grDevices</dc:relation>
  <dc:relation>LinkingTo: Rcpp, RcppArmadillo</dc:relation>
  <dc:relation>Suggests: testthat (&gt;= 3.0.0), knitr, rmarkdown</dc:relation>
  <dc:creator>Hsiu-Ting Yu &lt;hsiutingyu@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Hsiu-Ting Yu [aut, cre, cph] (ORCID:
    &lt;https://orcid.org/0000-0002-3668-8033&gt;)</dc:contributor>
  <dc:rights>GPL (&gt;= 3)</dc:rights>
  <dc:date>2026-10-08</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=silentema</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.silentema</dc:identifier>
  <dc:language>en-US</dc:language>
</oai_dc:dc>
