choxos
UserClaude Code plugin marketplace for biostatistics in R — 30 agents, 17 commands, and 45 skills spanning Bayesian modeling (Stan/PyMC/JAGS), indirect treatment comparisons (NMA/MAIC/STC/ML-NMR), tidy R workflows, and clinical trial simulation.
Categories
Indexed Skills (49)
bugs-fundamentals
Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.
hierarchical-models
Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
meta-analysis
Pairwise meta-analysis in R, including fixed and random effects, heterogeneity, bias checks, and forest plots.
model-diagnostics
MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.
pymc-fundamentals
Foundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
regression-models
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
stan-fundamentals
Foundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
survival-models
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
time-series-models
Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.
clinical-trial-design-patterns
Common clinical trial design patterns including multi-arm, multi-endpoint, adaptive, and stratified designs. Use when selecting or implementing trial designs.
group-sequential-methods
Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.
mediana-fundamentals
Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.
multiplicity-methods
Multiple testing procedures reference for clinical trials. Use when selecting or implementing multiplicity adjustments, gatekeeping procedures, or graphical approaches.
power-optimization-patterns
Direct and tradeoff-based optimization strategies for clinical trial design. Use when optimizing sample size, selecting design parameters, or performing sensitivity analysis.
simtrial-fundamentals
Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.
time-to-event-methods
Survival analysis methods including weighted logrank, MaxCombo, RMST, and milestone tests. Use when analyzing TTE data or choosing analysis methods for non-proportional hazards.
maic-methodology
Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.
ml-nmr-methodology
Deep methodology knowledge for ML-NMR including IPD/AgD integration, population adjustment, numerical integration, and prediction to target populations. Use when conducting or reviewing ML-NMR analyses.
nma-methodology
Deep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.
pairwise-ma-methodology
Deep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis. Use when conducting or reviewing pairwise MA.
stc-methodology
Deep methodology knowledge for STC including outcome regression, effect modifier selection, covariate centering, and comparison with MAIC. Use when conducting or reviewing STC analyses.
tidy-itc-workflow
Master tidy modelling patterns for ITC analyses following TMwR principles. Covers workflow structure, consistent interfaces, reproducibility best practices, and data validation. Use when setting up ITC analysis projects or building pipelines.
advanced-adaptive-trials
Adaptive trial designs in R, including platform, basket, MAMS, response-adaptive, and interim decision methods.
bayesian-modeling
Bayesian modeling in R with brms, rstanarm, priors, diagnostics, posterior checks, and model comparison.
causal-mediation
Causal mediation analysis in R, including direct and indirect effects, assumptions, and sensitivity analysis.
clinical-trials
Clinical trial design and analysis methods in R, including randomization, estimands, multiplicity, and reporting.
diagnostic-accuracy
Diagnostic accuracy analysis in R, including sensitivity, specificity, ROC curves, likelihood ratios, and decision curves.
epidemiology-methods
Epidemiological analysis methods in R for cohort, case-control, confounding control, and causal inference.
genomics-analysis
Genomics analysis in R with Bioconductor, differential expression, enrichment, batch correction, and single-cell workflows.
health-economics
Health economic analysis in R, including cost-effectiveness, QALYs, decision models, and budget impact.
ipd-meta-analysis
Individual participant data meta-analysis in R, including one-stage, two-stage, survival, and IPD with aggregate data.
mendelian-randomization
Mendelian randomization in R, including instrument selection, two-sample MR, pleiotropy checks, and sensitivity analysis.
model-evaluation
Model evaluation in R with performance metrics, calibration, ROC analysis, decision curves, and validation.
model-tuning
Hyperparameter tuning in tidymodels with grids, Bayesian optimization, racing, and workflow finalization.
network-meta-analysis
Network meta-analysis in R, including network setup, consistency, treatment rankings, and league tables.
pharmacokinetics
Pharmacokinetic and pharmacodynamic analysis in R, including NCA, compartmental modeling, and bioequivalence.
r-documentation-patterns
R documentation patterns with roxygen2, pkgdown, vignettes, examples, and package site structure.
real-world-evidence
Real-world evidence analysis in R, including target trial emulation, propensity scores, external controls, and bias analysis.
recipes-patterns
Feature engineering patterns with recipes, including imputation, encoding, normalization, interactions, and leakage control.
resampling-strategies
Resampling strategies in tidymodels, including validation splits, cross-validation, bootstrap, nested resampling, and grouped data.
roxygen2-pkgdown
R package documentation with roxygen2 and pkgdown, including reference topics, articles, and site configuration.
survival-analysis
Survival analysis in R, including Kaplan-Meier, Cox models, competing risks, RMST, and multi-state models.
tidymodels-review-patterns
Review patterns for tidymodels workflows, including leakage, resampling, tuning, metrics, and reproducibility.
tidymodels-workflow
Tidymodels workflow patterns with recipes, models, workflows, resampling, tuning, and final evaluation.
animation-patterns
Production animation patterns including reveal, transform, progressive reveal, emphasis, and cleanup patterns.
camera-techniques
Camera manipulation including zoom, pan, save/restore state, line width compensation, and focus transitions.
component-design
Reusable component patterns including VGroup subclasses, helper methods, encapsulated visualizations, and always_redraw patterns.
manim-fundamentals
Core Manim concepts including Scene lifecycle, Mobject hierarchy, coordinate systems, animation lifecycle, and rate functions.
math-typography
Mathematical rendering with MathTex, Tex, tex_to_color_map, custom equation classes, and formula animation patterns.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.