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vera-data-growth-pipelining

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更新日2026年5月16日 23:54

Complete skill family for longitudinal SEM focused on latent growth and change. Use when the user asks for latent growth models, latent change score models, nonlinear change, repeated latent trajectories, or longitudinal SEM in general. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

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VeraSuperHub/stat-research-pipeline

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SEM Longitudinal Change — Skill Family

Open-source skill.

This domain contains:

vera-data-growth-reviewing for setup checks and an initial latent growth or change model
vera-data-growth-generating for nonlinear trajectories, multigroup comparisons, parallel-process extensions, and manuscript output

Primary engines: lavaan for linear/latent growth basics, nlpsem when nonlinear longitudinal SEM is required.

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VeraSuperHub/stat-research-pipeline

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Runs class balance diagnostics and primary association tests for binary outcome variables (0/1, yes/no, survived/died, pass/fail). Produces proportion tables, class balance check with rare-event warning, descriptives by outcome level, chi-square test of independence with Cramer's V, Fisher's exact test when cell counts are small, odds ratio with 95% CI, and a mosaic or grouped bar chart. Outputs .R and .py scripts with 2 publication-quality plots. Triggered when user has a binary/dichotomous outcome and says "binary outcome," "survived or died," "yes or no," "pass or fail," "0/1," "classification," "binary DV," or names a binary variable like survived, admitted, defaulted, churned, diagnosed. Does not handle continuous, count, survival time, ordinal, repeated measures, or SEM.

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Complete skill family for confirmatory factor analysis (CFA) and measurement model evaluation. Use when the user asks for CFA, latent factor validation, measurement models, factor loadings, convergent/discriminant validity, or measurement invariance. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

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vera-data-cfa-reviewing

VeraSuperHub/stat-research-pipeline

Runs the CFA pipeline: collect indicator structure, check basic identification and estimator requirements, fit a primary CFA model, and report fit indices plus standardized loadings with a recommendation block for full SEM analysis. Trigger when the user asks for CFA, confirmatory factor analysis, measurement model testing, latent factor validation, factor loading assessment, construct validity, scale validation, measurement invariance check, or psychometric evaluation. Does not cover full structural path modeling (use vera-data-path-reviewing) or longitudinal change modeling (use vera-data-growth-reviewing).

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name	vera-data-growth-pipelining
description	Complete skill family for longitudinal SEM focused on latent growth and change. Use when the user asks for latent growth models, latent change score models, nonlinear change, repeated latent trajectories, or longitudinal SEM in general. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

SEM Longitudinal Change — Skill Family

Open-source skill.

This domain contains:

vera-data-growth-reviewing for setup checks and an initial latent growth or change model
vera-data-growth-generating for nonlinear trajectories, multigroup comparisons, parallel-process extensions, and manuscript output

Primary engines: lavaan for linear/latent growth basics, nlpsem when nonlinear longitudinal SEM is required.