Run any Skill in Manus with one click

vera-data-growth-pipelining

Stars1

Forks0

UpdatedMay 16, 2026 at 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.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Run Skill in Manus

Source

VeraSuperHub

VeraSuperHub/stat-research-pipeline

View GitHub Repository View Creator Repositories

Download

Run Skill in Manus

Related occupationsSOC

Based on SOC occupation classification

Data ScientistsComputer and Mathematical Occupations·SOC 15-2051

File Explorer

22 files

SKILL.md

readonly

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.

More from this repository

same repository

vera-data-application-pipelining

VeraSuperHub/stat-research-pipeline

End-to-end applied statistical research pipeline. Takes a research question and dataset, runs literature review, statistical analysis with parallel method tracks, and produces a complete manuscript (Markdown + LaTeX/PDF). Use when user says "application pipeline", "applied analysis", "analyze my data and write paper", "end-to-end analysis", or wants to go from raw data to manuscript. Covers all outcome types: continuous, binary, ordinal, nominal, count, survival, repeated measures, time series, multivariate, DOE, meta-analysis, and SEM. Human-in-the-loop by design: the skill handles the standardized workflow, while the user owns study framing, high-stakes judgment, and final sign-off.

2026-05-161

vera-data-binary-generating

VeraSuperHub/stat-research-pipeline

Server-side extension that completes the full analysis pipeline for binary outcome variables after vera-data-binary-reviewing has run. Adds remaining association tests (chi-square/Fisher's for additional predictors, point- biserial for continuous predictors), stratified odds ratio analysis with Breslow-Day test and forest plot, full modeling (logistic regression with OR and 95% CI, Hosmer-Lemeshow GOF, pseudo-R2, ROC curve with AUC, classification table, tree-based classification with CART/RF/GBM), and cross-method variable importance comparison on a 0-100 unified scale. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-binary-reviewing completes and its PART 0–2 artifacts (code.R, code.

2026-05-161

vera-data-binary-reviewing

VeraSuperHub/stat-research-pipeline

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.

2026-05-161

vera-data-cfa-generating

VeraSuperHub/stat-research-pipeline

Full CFA skill for measurement-model evaluation. Extends the initial CFA fit with reliability, convergent/discriminant validity, alternative-model comparison, modification-index review, subgroup invariance testing, and manuscript-ready methods/results. Trigger after vera-data-cfa-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

2026-05-161

vera-data-cfa-pipelining

VeraSuperHub/stat-research-pipeline

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.

2026-05-161

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).

2026-05-161

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.