Introduction | Installation | What diagnosis checks | A minimal example | Instrument strength | Negative-control validity | Proceeding without instruments or controls | Further reading | Session information

Introduction | Installation | Exposure instruments from GWAS summary statistics | Quality-control the summary statistics | Build a polygenic score with LDpred2 | Score a panel of pre-computed weights | Verify instrument strength | Mediator instruments | Scan for cis-eQTLs | Build a genetically predicted mediator (GReX-style) | Negative controls from high-dimensional panels | Methylation beta to M-values | Residualise on covariates | Extract negative-control factors with PCA | Apply FUSION-style TWAS weights | Importing a SummarizedExperiment | Session information

Introduction | Installation | What this package is for | Quick start | Step 1: Diagnose | Step 2: Estimate | Step 3: Train a texture model once | Step 4: Sensitivity analysis | Step 5: Recommend | Step 6: Prospective analysis | Survival outcomes | Putting it together | Further reading | Session information

Introduction | Installation | What sensitivity analysis does | A minimal example | Inferring confounding from the data | Inferred vs. default confounding | Further reading | Session information

Introduction | Installation | Time-to-event outcomes in ICONIC | Two effect scales | COCA incompatibility | A minimal example | Cox log-HR scale | RMST scale | Diagnosis and eligibility | Further reading | Session information

Introduction | Installation | Two simulation modes | The hybrid GAN + copula architecture | Training the texture model | Attaching the texture model | When to use each mode | Further reading | Session information