Run Bayesian regression models and validate causal DAGs in R.
npx skills add https://github.com/laurenoconnelllab/pTRAPPING --skill r-bayes-laurenoconnelllab
Please help me install this Agent Skill. Skill: r-bayes Source: https://github.com/laurenoconnelllab/pTRAPPING/tree/main/.claude/skills/r-bayes Command: npx skills add https://github.com/laurenoconnelllab/pTRAPPING --skill r-bayes-laurenoconnelllab
This Skill provides tools for Bayesian inference, causal graph validation, and effect estimation in R, simplifying complex statistical workflows.
Use the r-bayes skill to run a Bayesian logistic regression with a random intercept, interpret the model’s effects, and visualize the posterior distributions.
High-intent search queries and answers about installing and using this skill.
Bayesian causal inference requires structuring effect estimation through DAG validation and posterior visualization. You can use R packages like brms and dagitty to validate causal graphs, configure multilevel models with specified priors, and interpret effect sizes for complex data analysis.
To estimate marginal effects and odds ratios from posterior distributions, extract and interpret the model outputs using packages like marginaleffects and tidybayes. This approach simplifies generating understandable summaries and publication-ready plots from Bayesian regression models.
Yes, you can use cmdstanr and brms to configure and run multilevel models with random effects. These dependencies allow you to specify priors for Bayesian regression and generate posterior distribution visualizations for structured effect estimation.
The best way to visualize causal DAGs and posterior distributions in R is using ggdag and bayesplot. These packages enable you to create and validate causal graphs, visualize model assumptions, and plot posterior distributions to inform model specification.
You need dagitty and ggdag to validate causal graphs before Bayesian regression because they inform model specification and causal inference. Creating and visualizing these directed acyclic graphs ensures your structured effect estimation accurately reflects your causal assumptions.
gnobletRun Bayesian inference workflows with brms for multilevel models and posterior diagnostics.
fundamental-physicsVisualize Bayesian posterior samples from nested sampling and MCMC chains.
xjtulycBuild Bayesian models with PyMC 5.x and ArviZ using NUTS sampling.
HaipingXuImplement causal inference methods in Python and Stata with diagnostic checks.
