[hsiutingyu] umg 0.6.1

hsiutingyu@gmail.com (Hsiu-Ting Yu) — Wed, 14 Oct 2026 10:20:06 GMT

Constructs, validates, lays out, and renders Unified Model Graphs (UMG), a formal graphical grammar for statistical models in psychology. Vertices are typed by observability, support, and inferential role; edges distinguish stochastic, symmetric, deterministic, and mixing dependence; plates encode replication and hierarchy. Diagrams can be themed and rendered with base 'grid' graphics, with 'ggplot2', as 'TikZ' code, or as 'Graphviz' DOT, and can be constructed either from one-line motif builders for many model families or automatically from 'lavaan', 'lme4', 'mirt', 'blavaan', 'brms', 'OpenMx', and 'qgraph' model objects. A diagram can be inspected as a data frame, summarised, and exported back to 'lavaan' model syntax, making the model-diagram correspondence operational in both directions. Each diagram also exposes identification diagnostics (scaling checks, a graphical counting rule, and a d-separation reader) and generates the exploratory display scaffolds implied by the model-data duality. The notation builds on established results for graphical and structural equation models: plate notation follows Buntine (1994) ; the counting rule and scaling checks follow Bollen (1989) ; and the independence reader implements the d-separation criterion of Geiger, Verma and Pearl (1990) through the equivalent ancestral graph criterion of Lauritzen, Dawid, Larsen and Leimer (1990) .

[hsiutingyu] silentema 1.0.0

hsiutingyu@gmail.com (Hsiu-Ting Yu) — Mon, 28 Sep 2026 05:20:03 GMT

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) ; 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) ); estimates the temporal and contemporaneous networks from answered adjacent prompts with the half-panel jackknife of Dhaene and Jochmans (2015) , 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) ; 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) ; 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 .

hsiutingyu.r-universe.dev

[hsiutingyu] umg 0.6.1

[hsiutingyu] silentema 1.0.0