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
11Stars on the repository
60Mods indexed here, across every type
3mo agoLast push, which is what freshness is scored on
MITLicence, which decides whether bodies are shown
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
06Agent
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
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
Agent
Expert in multiple testing procedures and multiplicity adjustment optimization. Handles Holm, Hochberg, Hommel, chain procedures, and gatekeeping strategies for Type I error control.
11 3mo ago A 38 tokens
original MIT
Agent
Power and sample size optimization using direct and tradeoff-based strategies. Performs qualitative and quantitative sensitivity assessments.
11 3mo ago A 24 tokens
original MIT
Agent
Entry point for clinical trial simulation requests. Routes to appropriate specialists based on trial design (TTE, CSE, multiplicity, group sequential). Use PROACTIVELY for routing.
11 3mo ago A 41 tokens
original MIT
Agent
Time-to-event simulation specialist using simtrial. Handles piecewise exponential survival, weighted logrank tests, MaxCombo, RMST, and milestone analyses. Use PROACTIVELY for survival/TTE simulations.
11 3mo ago A 45 tokens
original MIT
Agent
Master ITC strategist specializing in evidence synthesis methodology, study design assessment, and indirect treatment comparison method selection. Helps choose between pairwise MA, NMA, MAIC, STC, and ML-NMR based on data availability and assumptions. Use PROACTIVELY when planning ITC analyses or needing guidance on…
11 3mo ago A 70 tokens
original MIT
Agent
Expert ITC R code reviewer who identifies methodological errors, statistical issues, and code quality problems. Offers comprehensive or summary reviews, and can amend code (saved to subfolder to prevent data loss). Use PROACTIVELY when reviewing ITC analysis code.
11 3mo ago A 57 tokens
original MIT
Agent
Expert in Matching-Adjusted Indirect Comparison using the maicplus package. Handles weight estimation, anchored/unanchored MAIC, binary/continuous/TTE endpoints, ESS diagnostics, and Bucher comparisons. Use PROACTIVELY for MAIC analyses.
11 3mo ago A 56 tokens
original MIT
Agent
Expert in Multilevel Network Meta-Regression using the multinma package. Handles combined IPD/AgD networks, population adjustment, covariate integration, and prediction to target populations. Use PROACTIVELY for ML-NMR analyses.
11 3mo ago A 54 tokens
original MIT
Agent
Expert in frequentist and Bayesian network meta-analysis using netmeta and gemtc packages. Handles network visualization, consistency assessment, treatment rankings, and league tables. Use PROACTIVELY for NMA tasks involving multiple treatment comparisons.
11 3mo ago A 50 tokens
original MIT
Agent
Expert in frequentist and Bayesian pairwise meta-analysis using meta, metafor, and bayesmeta packages. Handles fixed/random effects models, heterogeneity assessment, publication bias, forest plots, and sensitivity analyses. Use PROACTIVELY for pairwise MA tasks.
11 3mo ago A 60 tokens
original MIT
Agent
Expert in Simulated Treatment Comparison using transparent outcome-regression methods. Handles anchored and unanchored STC for binary, continuous, count, and survival outcomes with explicit assumptions and sensitivity analyses. Use PROACTIVELY for STC analyses.
11 3mo ago A 53 tokens
original MIT
Agent
Expert biostatistician specializing in clinical trials, survival analysis, epidemiology, genomics, and regulatory-compliant medical statistics. Masters frequentist and Bayesian methods, survival models, mixed effects, meta-analysis, and diagnostic accuracy. Use PROACTIVELY for clinical trial design, survival analysis…
11 3mo ago A 73 tokens
original MIT
Agent
Expert in tidyverse data manipulation specializing in dplyr, tidyr, purrr, and related packages for data transformation, cleaning, and reshaping. Masters complex joins, pivoting, list-columns, and functional programming for data preparation. Use PROACTIVELY for data cleaning, transformation, aggregation, or complex…
11 3mo ago A 72 tokens
original MIT
Agent
Expert in recipes-based feature engineering for machine learning and statistical modeling. Masters preprocessing transformations, handling missing data, encoding strategies, feature extraction, and domain-specific feature creation. Use PROACTIVELY for data preprocessing, feature creation, handling missing values, or…
11 3mo ago A 59 tokens
original MIT