Articulate estimand-driven statistical analysis plans in R.
npx skills add https://github.com/blankuzr/R-Skills --skill r-stats
Please help me install this Agent Skill. Skill: r-stats Source: https://github.com/blankuzr/R-Skills/tree/main/gpt/skills/r-stats Command: npx skills add https://github.com/blankuzr/R-Skills --skill r-stats
Guides teams in articulating estimand-driven R statistics plans, enabling transparent, auditable, and reproducible analysis workflows.
Describe your estimand and data structure to generate a complete, reproducible R-stats analysis plan.
High-intent search queries and answers about installing and using this skill.
To structure an estimand-driven statistical analysis plan in R, you articulate your estimand and data structure to generate modular templates with explicit naming, validation hooks, and cross-tool guidance spanning base stats, marginal effects, and easystats ecosystems for reproducible workflows.
The best way to integrate causal inference and Bayesian modeling workflows in R is using a disciplined methods contract that guides method selection, applies diagnostics, and ensures comprehensive result communication across complex analyses.
Handling missing data and bootstrap diagnostics for mixed-effects models in R requires integrated pathways with validation hooks that apply diagnostics and reporting guidance across survival, meta-analysis, and mixed-effects contexts.
Yes, this R workflow approach supports structural equation modeling and marginal effects estimation through reusable templates and cross-tool guidance that ensures transparent and auditable analysis workflows across base stats and easystats ecosystems.
Yes, you can generate reproducible R analysis plans for survival and meta-analysis reporting by applying modular templates with validation hooks and explicit estimand naming to satisfy a disciplined methods contract across comprehensive result communication contexts.
AlterLab-IEUMap research questions and variables to statistical tests with analysis plans.
shaitamam-80Plan meta-analyses with effect-size selection, heterogeneity assessment, and R code templates.
jhelvyFit Bayesian multilevel models with brms and visualize causal DAGs in R.
laurenoconnelllabRun Bayesian regression models and validate causal DAGs in R.
