Bayesian Modeling
Bayesian modeling in R with brms, rstanarm, priors, diagnostics, posterior checks, and model comparison.
Install
$ agentstack add skill-choxos-biostatagent-bayesian-modeling ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-choxos-biostatagent-bayesian-modeling) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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How agent discovery & health will work →About
Bayesian Modeling in R
Overview
Comprehensive Bayesian statistical modeling using Stan-based packages (brms, rstanarm), covering prior specification, posterior analysis, model comparison, and Bayesian workflow best practices.
brms: Bayesian Regression Models
Basic Models
library(brms)
# Linear regression
fit 0")
hypothesis(fit, "x1 > x2")
hypothesis(fit, "x1 + x2 > 0")
# Multiple hypotheses
hypothesis(fit, c("x1 > 0", "x2 > 0", "x1 > x2"))Posterior Predictive Checks
library(bayesplot)
# Density overlay
pp_check(fit, type = "dens_overlay", ndraws = 50)
# Histogram
pp_check(fit, type = "hist", ndraws = 8)
# Error scatter
pp_check(fit, type = "error_scatter_avg")
# Intervals
pp_check(fit, type = "intervals")
# Stat comparison
pp_check(fit, type = "stat", stat = "mean")
pp_check(fit, type = "stat_2d", stat = c("mean", "sd"))MCMC Diagnostics
library(bayesplot)
# Trace plots
mcmc_trace(fit)
# Rhat
rhat(fit)
mcmc_rhat(rhat(fit))
# Effective sample size
neff_ratio(fit)
mcmc_neff(neff_ratio(fit))
# Pairs plot (divergences)
mcmc_pairs(fit, pars = c("b_x1", "b_x2", "sigma"))
# Energy
mcmc_nuts_energy(nuts_params(fit))Model Comparison
LOO Cross-Validation
# LOO-CV
loo_fit1 0")
# 7. Predictions
conditional_effects(fit)Key Packages Summary
| Package | Purpose | |---------|---------| | brms | General Bayesian regression | | rstanarm | Applied regression models | | bayesplot | MCMC visualization | | loo | Model comparison | | bridgesampling | Bayes factors | | tidybayes | Tidy Bayesian analysis | | posterior | Posterior manipulation |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: choxos
- Source: choxos/BiostatAgent
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.