GitHub
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.
☆ 0View skill
choxos AI Skills
GitHub ↗Browse published AI skills maintained by choxos. Compare skill descriptions and follow each source repository for setup instructions.
Author statistics
Indexed skills
22Repository stars across indexed skills
0Repository forks
0Average repository stars per indexed skill
0Repository stars may repeat across skills from the same repository.
Popular categories
Recent indexing activity
Browse skills
GitHub
multiplicity-methods
Multiple testing procedures reference for clinical trials. Use when selecting or implementing multiplicity adjustments, gatekeeping procedures, or graphical approaches.
☆ 0View skill
GitHub
time-series-models
Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
stan-fundamentals
Foundational knowledge for writing Stan 2.37 models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
nma-methodology
Deep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.
☆ 0View skill
GitHub
hierarchical-models
Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
☆ 0View skill
GitHub
model-diagnostics
MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.
☆ 0View skill
GitHub
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...
☆ 0View skill
GitHub
meta-analysis
Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.
☆ 0View skill
GitHub
survival-models
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
regression-models
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
pymc-fundamentals
Foundational knowledge for writing PyMC 5 models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
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.
☆ 0View skill
GitHub
stc-methodology
Deep methodology knowledge for STC including outcome regression approach, effect modifier selection, covariate centering, and comparison with MAIC. Use when conducting or reviewing STC analyses.
☆ 0View skill
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.