BiostatAgent
Comprehensive Biostatistics Agent Ecosystem -- 30 specialized agents, 17 workflow commands, and 45 methodology skills for R-based statistical analysis
A unified Claude Code plugin marketplace consolidating four specialized biostatistics plugins:
| Plugin | Focus | Agents | Commands | Skills |
|---|---|---|---|---|
| bayesian-modeling | Bayesian inference (Stan, PyMC, JAGS) | 6 | 3 | 9 |
| itc-modeling | Indirect treatment comparisons | 7 | 2 | 6 |
| r-tidy-modeling | Tidy R workflows & biostatistics | 10 | 7 | 23 |
| clinical-trial-simulation | Clinical trial simulation | 7 | 5 | 7 |
| Total | 30 | 17 | 45 |
Quick Start
1. Add the Marketplace
/plugin marketplace add choxos/BiostatAgent
2. Install Plugins
Install all plugins or select specific ones:
# Install all
/plugin install bayesian-modeling itc-modeling r-tidy-modeling clinical-trial-simulation
# Or install individually
/plugin install bayesian-modeling
/plugin install itc-modeling
/plugin install r-tidy-modeling
/plugin install clinical-trial-simulation
3. Install R Dependencies
# Core dependencies (install as needed)
install.packages(c(
# Bayesian modeling
"cmdstanr", "rstan", "R2jags", "R2WinBUGS", "bayesplot", "loo",
# ITC/NMA
"meta", "netmeta", "gemtc", "multinma", "maicplus",
# Tidy modeling
"tidyverse", "tidymodels", "recipes", "parsnip", "workflows",
# Clinical trials
"simtrial", "Mediana", "gsDesign2", "survival"
))
Plugins Overview
1. Bayesian Modeling (bayesian-modeling)
Create, review, and validate Bayesian models across four languages:
- Stan 2.37 -- Modern HMC/NUTS sampling with cmdstanr
- PyMC 5 -- Python-native Bayesian modeling with ArviZ
- JAGS -- Cross-platform Gibbs sampling with R2jags
- WinBUGS -- Classic BUGS implementation with R2WinBUGS
Commands:
| Command | Description |
|---|---|
/create-model | Interactive model creation workflow |
/review-model | Review existing models for correctness |
/run-diagnostics | Test model execution with synthetic data |
Agents: model-architect, stan-specialist, pymc-specialist, bugs-specialist, model-reviewer, test-runner
Skills: stan-fundamentals, pymc-fundamentals, bugs-fundamentals, hierarchical-models, regression-models, time-series-models, survival-models, meta-analysis, model-diagnostics
2. ITC Modeling (itc-modeling)
Expert agents for indirect treatment comparison following NICE DSU guidance:
- Pairwise Meta-Analysis -- Fixed/random effects with meta, metafor, bayesmeta
- Network Meta-Analysis -- Frequentist (netmeta) and Bayesian (gemtc)
- MAIC -- Matching-adjusted indirect comparison with maicplus
- STC -- Simulated treatment comparison
- ML-NMR -- Multilevel network meta-regression with multinma
Commands:
| Command | Description |
|---|---|
/itc-analysis | Full ITC workflow from method selection to results |
/itc-review | Review existing ITC code for methodological issues |
Agents: itc-architect, pairwise-meta-analyst, nma-specialist, maic-specialist, stc-specialist, ml-nmr-specialist, itc-code-reviewer
Skills: tidy-itc-workflow, pairwise-ma-methodology, nma-methodology, maic-methodology, stc-methodology, ml-nmr-methodology
3. R Tidy Modeling (r-tidy-modeling)
Comprehensive R data science and biostatistics following tidyverse and tidymodels best practices:
- Data Wrangling -- dplyr, tidyr, data transformation
- Feature Engineering -- recipes, preprocessing, transformations
- Model Building -- parsnip, workflows, tidymodels
- Visualization -- ggplot2, publication-ready figures
- Reporting -- Quarto, R Markdown, reproducible reports
- Biostatistics -- Clinical trials, survival analysis, epidemiology, meta-analysis, genomics, pharmacokinetics, health economics, and more
Commands:
| Command | Description |
|---|---|
/r-analysis | End-to-end analysis workflow |
/r-code-review | Review R code for best practices |
/r-model-comparison | Compare multiple models |
/r-clinical-analysis | Clinical trial analysis workflow |
/r-project-setup | Set up reproducible R project |
/r-doc-generate | Generate documentation |
/r-tutorial-create | Create tutorials from code |
Agents: r-data-architect, tidymodels-engineer, feature-engineer, biostatistician, data-wrangler, viz-specialist, reporting-engineer, r-code-reviewer, r-docs-architect, r-tutorial-engineer
Skills (23):