r-tidy-modeling

Comprehensive data analysis workflow using tidy principles. This command orchestrates multiple agents to perform end-to-end data analysis.

Regulatory-compliant clinical trial analysis workflow. This command orchestrates the **biostatistician** agent with support from **viz-specialist** and **reporting-engineer** for comprehensive clinical data analysis.

Comprehensive code review for R scripts and packages. This command uses the **r-code-reviewer** agent (Opus) to perform thorough quality assessment.

Generate comprehensive documentation from R codebases. This command uses the **r-docs-architect** agent to create roxygen2 documentation, README files, pkgdown sites, and architecture documentation.

Systematic model comparison using tidymodels workflow sets. This command orchestrates the **tidymodels-engineer** and **feature-engineer** agents to build and compare multiple models.

Initialize a new R data science project with best practices. This command uses the **r-data-architect** agent to create a well-structured, reproducible project foundation.

Create step-by-step tutorials and educational content from R code. This command uses the **r-tutorial-engineer** agent to transform code into progressive learning experiences.

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, epidemiological studies, or medical statistics questions.

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 data manipulation tasks.

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 preparing data for modeling.

Expert R code reviewer specializing in tidyverse style, performance optimization, package development patterns, testing with testthat, TMwR anti-pattern detection, and documentation standards. Conducts comprehensive code audits for quality, maintainability, tidymodels workflow compliance, and best practices. Use PROACTIVELY for reviewing R code, tidymodels workflows, or ensuring code quality standards.

Strategic R project architect specializing in enterprise data science architecture, project structure design, workflow orchestration with targets, reproducibility with renv, and technology selection. Masters tidymodels ecosystems, pipeline design, and deployment strategies. Use PROACTIVELY when planning new R projects, establishing team standards, or architecting complex analytical workflows.

Technical documentation specialist for R projects, creating comprehensive manuals, package documentation with pkgdown, vignettes, README files, and architecture documentation. Masters roxygen2, pkgdown configuration, and technical writing for R codebases. Use PROACTIVELY for generating package documentation, creating README files, or building pkgdown sites.

Tutorial and educational content creator for R, transforming code into progressive learning experiences with exercises, interactive learnr tutorials, cookbooks, and onboarding guides. Masters instructional design for technical content. Use PROACTIVELY for creating tutorials, learning materials, onboarding documentation, or educational content.

Expert in R reproducible reporting with RMarkdown, Quarto, and Shiny for creating dynamic documents, presentations, dashboards, and interactive applications. Masters parameterized reports, automated pipelines, and multi-format publishing. Use PROACTIVELY for creating reports, documents, presentations, dashboards, or Shiny applications.

Expert tidymodels practitioner specializing in production-ready machine learning pipelines using parsnip, workflows, tune, and the extended tidymodels ecosystem. Masters model specification, hyperparameter tuning, resampling strategies, and model stacking. Use PROACTIVELY for building ML models, tuning hyperparameters, comparing models, or implementing ensemble methods.

Expert in R data visualization specializing in ggplot2, publication-quality graphics, and interactive visualizations. Masters the grammar of graphics, statistical plots, scientific figures, and domain-specific visualizations for biostatistics and data science. Use PROACTIVELY for creating plots, statistical graphics, or publication-quality figures.

Advanced adaptive clinical trial designs including platform trials, basket and umbrella trials, response-adaptive randomization, multi-arm multi-stage designs, Bayesian adaptive methods, and sample size re-estimation techniques.

Comprehensive Bayesian statistical modeling using Stan-based packages (brms, rstanarm), covering prior specification, posterior analysis, model comparison, and Bayesian workflow best practices.

Causal mediation analysis methods for decomposing total effects into direct and indirect effects. Covers traditional approaches, natural effect models, sensitivity analysis for unmeasured confounding, mediation with survival outcomes, and comprehensive causal mediation frameworks.

Comprehensive clinical trial design and analysis methods in R covering sample size calculation, randomization, interim analyses, multiplicity adjustment, and regulatory-compliant statistical methods.

Comprehensive diagnostic test accuracy analysis covering ROC curve analysis, optimal cutpoint determination, sensitivity and specificity estimation, likelihood ratios, decision curve analysis, inter-rater reliability measures, and diagnostic meta-analysis.

Comprehensive epidemiological analysis methods covering study designs, measures of association, confounding control, propensity scores, and causal inference using R.

Comprehensive genomics and bioinformatics statistical methods using Bioconductor packages. Covers differential expression analysis, pathway enrichment, and visualization for RNA-seq and microarray data.

Health economic evaluation methods covering cost-effectiveness analysis (CEA), quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICERs), budget impact analysis, Markov cohort models, partitioned survival analysis, probabilistic sensitivity analysis, and value of information analysis.

Individual participant data (IPD) meta-analysis methods for synthesizing patient-level data across studies. Covers one-stage and two-stage approaches, mixed-effects models, combining IPD with aggregate data, treatment-covariate interactions, and handling missing data in multi-study settings.

Mendelian randomization (MR) methods for causal inference using genetic variants as instrumental variables. Covers instrument selection, two-sample MR, sensitivity analyses, pleiotropy assessment, multivariable MR, and advanced methods for robust causal inference.

Comprehensive pairwise meta-analysis methods covering effect size calculation, fixed and random effects models, heterogeneity assessment, publication bias detection, subgroup analysis, meta-regression, and sensitivity analyses for synthesizing evidence across studies.

Comprehensive model evaluation using yardstick and related packages. Covers metrics for classification, regression, and survival outcomes, plus calibration and uncertainty quantification.

Comprehensive hyperparameter optimization using the tune package. Covers grid search, iterative search, racing methods, and Bayesian optimization.

Network meta-analysis (NMA) methods for comparing multiple treatments simultaneously using direct and indirect evidence. Covers network structure assessment, frequentist and Bayesian NMA approaches, consistency evaluation, treatment rankings, and visualization techniques.

Comprehensive pharmacokinetic (PK) and pharmacodynamic (PD) modeling in R covering non-compartmental analysis (NCA), compartmental PK modeling, population PK with nonlinear mixed effects, bioequivalence assessment, PK/PD modeling, and drug-drug interaction evaluation.

Best practices and patterns for documenting R code, packages, and projects. Covers README files, code comments, function documentation, and project-level documentation.

Methods for analyzing real-world data (RWD) to generate real-world evidence (RWE). Covers target trial emulation, comparative effectiveness research, propensity score methods for observational data, external control arms, bias quantification, and sensitivity analysis for unmeasured confounding.

Comprehensive patterns for feature engineering using the recipes package. Covers preprocessing steps for numeric, categorical, and text data while preventing information leakage.

Comprehensive guide to resampling methods for model validation using the rsample package. Covers cross-validation, bootstrapping, and specialized resampling for time series and grouped data.

Complete reference for roxygen2 documentation syntax and pkgdown site configuration. Covers all roxygen2 tags, cross-referencing, and advanced pkgdown customization.

Comprehensive survival analysis methods in R covering Kaplan-Meier estimation, Cox proportional hazards models, parametric survival models, and advanced techniques for time-to-event data.

Anti-pattern detection and best practices for tidymodels workflows based on "Tidy Modeling with R" (TMwR) principles. This skill enables systematic code review for data leakage, resampling violations, workflow issues, evaluation problems, and reproducibility concerns.

Core workflow patterns for building machine learning models using the tidymodels ecosystem. Covers the complete pipeline from data splitting through model deployment.