Provide reusable R model-building patterns for Bayesian, SEM, and meta-analysis workflows.
npx skills add https://github.com/blankuzr/R-Skills --skill r-model-builder
Please help me install this Agent Skill. Skill: r-model-builder Source: https://github.com/blankuzr/R-Skills/tree/main/gpt/skills/r-model-builder Command: npx skills add https://github.com/blankuzr/R-Skills --skill r-model-builder
Reusable, battle-tested R modeling patterns that let teams build advanced model-helper layers (specification, formula/builders, data-slicing, fit wrappers, diagnostics, and post-estimation adapters) in a consistent, testable way across Bayesian, causal-design, meta-analysis, lavaan SEM, and tidymodels workflows.
Define a modeling spec, build the right-hand side from validated terms, prepare a ready-to-fit data frame, fit the model with data =, and surface tidy post-estimation outputs.
High-intent search queries and answers about installing and using this skill.
Reusable R model-building patterns separate estimand, covariates, and random effects into modular spec and formula builders, ensuring reliable modeling across Bayesian, lavaan SEM, and tidymodels workflows with explicit boundaries between design, fit, and reporting surfaces.
Managing data-materialization boundaries involves collecting only model-ready frames from large datasets or Arrow Parquet sources, passing them explicitly via data = to fit wrappers, which keeps the path from specification to final reporting clear and efficient.
Yes, you can use tidymodels and metafor together within a layered R modeling architecture that maintains fit, diagnostics, post-estimation, and deployment adapters as independent surfaces, enabling robust testing and reusable components across meta-analysis and tidymodels workflows.
Modular formula builders separate estimands, covariates, and random effects for causal-design models in R by enforcing a multi-layer contract with explicit boundaries, allowing reliable specification across diverse families like brms and marginaleffects.
Yes, this R modeling approach supports post-estimation summaries and deployment-ready adapters by keeping fit, diagnostics, and reporting as independent surfaces, enabling robust testing and reusable components across Bayesian, SEM, and meta-analytic contexts.
gnobletRun Bayesian inference workflows with brms for multilevel models and posterior diagnostics.
scdenneyDiagnose conjoint studies using a systematic checklist covering design, estimation, and validity.
impact-initiativesFit brms multilevel Bayesian models with DAG-based causal assumption checks.
matheus-rechConduct Bayesian meta-analysis with priors and posterior inference in R tools.
