choxos

Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.

Foundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.

Foundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.

Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.

Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.

Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.

Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.

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.

Bioconductor-specific package submission requirements, BiocCheck validation, version numbering, and review process beyond standard CRAN requirements

Complete guide to CRAN submission including policies, pre-submission checks, common issues, and the submission process

Comprehensive guide to including data in R packages, covering exported data, internal data, raw files, documentation, and CRAN size limits

Complete guide to managing R package dependencies, including Imports vs Suggests, namespace imports, and handling dependencies in code, tests, examples, and vignettes

Automated CI/CD workflows for R packages including R CMD check across platforms, test coverage reporting, and pkgdown deployment

Create professional hexagonal package logos using hexSticker with Google Fonts, proper dimensions, and automated generation scripts

R package licensing including common licenses, compatibility matrix, proper license file setup, and handling bundled code

Understanding R package directory layout, package states, required and optional files, and build configuration patterns

Core Manim concepts including Scene lifecycle, Mobject hierarchy, coordinate systems, animation lifecycle, and rate functions.

Mathematical rendering with MathTex, Tex, tex_to_color_map, custom equation classes, and formula animation patterns.

Production animation patterns including reveal, transform, progressive reveal, emphasis, and cleanup patterns.

Camera manipulation including zoom, pan, save/restore state, line width compensation, and focus transitions.

Reusable component patterns including VGroup subclasses, helper methods, encapsulated visualizations, and always_redraw patterns.

3Blue1Brown-style production standards including color palettes, timing guidelines, positioning rules, cleanup patterns, and typography standards.

Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.

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.

Deep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.

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.

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.

Foundational knowledge for writing PyMC 5 models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.

Foundational knowledge for writing Stan 2.37 models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.

Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.

Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.

Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.

Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.