Package: silentema
Type: Package
Title: Dynamic Missingness Graphs and Sensitivity Analysis for EMA Data
Version: 1.0.0
Authors@R: person(given = "Hsiu-Ting", family = "Yu", email = "hsiutingyu@gmail.com",
                  role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-3668-8033"))
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) <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/>.
License: GPL (>= 3)
URL: https://github.com/hsiutingyu/silentema,
        https://hsiutingyu.github.io/silentema/, https://osf.io/x6d2t/
BugReports: https://github.com/hsiutingyu/silentema/issues
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.7), stats, graphics, grDevices
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.3.1
Config/testthat/edition: 3
Repository: https://hsiutingyu.r-universe.dev
Date/Publication: 2026-09-28 05:20:03 UTC
RemoteUrl: https://github.com/hsiutingyu/silentema
RemoteRef: HEAD
RemoteSha: 1966db3533db80b80309dc5d34036e62d8e49c2c
NeedsCompilation: yes
Packaged: 2026-10-08 11:46:15 UTC; root
Author: Hsiu-Ting Yu [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-3668-8033>)
Maintainer: Hsiu-Ting Yu <hsiutingyu@gmail.com>