Plugin Claude Code
Plugin marketplace listing 4 plugins: bayesian-modeling, itc-modeling, r-tidy-modeling, clinical-trial-simulation.
11 3mo ago A
tokens not measured
original MIT
Plugin Claude Code
Create, review, and validate Bayesian models in Stan, PyMC, JAGS, and WinBUGS.
11 3mo ago A
tokens not measured
original MIT
Agent
Expert in WinBUGS and JAGS model specification. Understands precision parameterization, d-prefix distributions, declarative syntax, and R integration via R2WinBUGS and R2jags packages.
11 3mo ago A 45 tokens
original MIT
Agent
Orchestrates Bayesian model creation and review. Routes to specialized agents based on user needs, language choice (Stan/JAGS/WinBUGS/PyMC), and model type. Entry point for all modeling tasks.
11 3mo ago A 47 tokens
original MIT
Agent
Reviews and validates Bayesian model specifications for correctness, efficiency, and best practices. Identifies syntax errors, missing priors, parameterization issues, and suggests improvements.
11 3mo ago A 36 tokens
original MIT
Agent
Expert in current PyMC for Bayesian modeling in Python. Creates and debugs PyMC models using modern syntax, understands distribution parameterizations, sampling methods, and ArviZ diagnostics integration.
11 3mo ago A 43 tokens
original MIT
Agent
Expert in modern Stan programming for Bayesian inference. Creates and debugs Stan models using cmdstanr, understands all 7 program blocks, HMC/NUTS optimization, and current Stan syntax.
11 3mo ago A 43 tokens
original MIT
test-runner
08Agent
Executes Stan, JAGS, WinBUGS, and PyMC models with test data to validate syntax and sampling. Generates synthetic data, runs short MCMC chains, and reports convergence diagnostics.
11 3mo ago A 43 tokens
original MIT
create-model
09Command
Interactive workflow for creating Bayesian models in Stan, JAGS, WinBUGS, or PyMC.
11 3mo ago A 22 tokens
original MIT
review-model
10Command
Review and improve existing Bayesian models for correctness, efficiency, and best practices.
11 3mo ago A 17 tokens
original MIT
Command
Execute Bayesian models with test data and report convergence diagnostics.
11 3mo ago A 14 tokens
original MIT
Skill Claude CodeCodex
Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.
11 3mo ago A 42 tokens
original MIT
Skill Claude CodeCodex
Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
11 3mo ago A 28 tokens
original MIT
Skill Claude CodeCodex
Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.
11 3mo ago A 26 tokens
original MIT
Skill Claude CodeCodex
MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.
11 3mo ago A 26 tokens
original MIT
Skill Claude CodeCodex
Foundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
11 3mo ago A 39 tokens
original MIT
Skill Claude CodeCodex
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
11 3mo ago A 31 tokens
original MIT
Skill Claude CodeCodex
Foundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
11 3mo ago A 35 tokens
original MIT
Skill Claude CodeCodex
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
11 3mo ago A 30 tokens
original MIT
Skill Claude CodeCodex
Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.
11 3mo ago A 32 tokens
original MIT
Plugin Claude Code
Clinical trial simulation workflows for power, sample size, group sequential designs, and multiplicity.
11 3mo ago A
tokens not measured
original MIT
Agent
Reviews R code for clinical trial simulations. Validates simtrial and Mediana usage, checks statistical assumptions, ensures reproducibility.
11 3mo ago A 29 tokens
original MIT
Agent
Clinical Scenario Evaluation specialist using Mediana. Builds Data, Analysis, and Evaluation models for comprehensive trial simulations. Use PROACTIVELY for multi-scenario power analyses.
11 3mo ago A 38 tokens
original MIT
Agent
Group sequential design specialist. Handles interim analyses, alpha spending functions, futility stopping rules, and information fraction calculations.
11 3mo ago A 28 tokens
original MIT