Installation | Introduction | Input alignment | Collaborative graphical lasso | Comparing cell types | Exposure-restricted networks | Longitudinal networks | Mediation terminology | Composition and provenance | References | Session information

Installation | Introduction | Prepare a validated container | Fit the donor-pseudobulk model | Inspect results without changing the family | Sensitivity analyses | Composition is a different outcome | Legacy decomposition interfaces | Full reproducible analysis | Session information

Installation | Introduction | Unit of analysis and estimand | Required data contract | Canonical donor-pseudobulk model | Multiplicity and output interpretation | Repeated samples and composition | Developmental decomposition interfaces | Public source and reproducibility | Session information

Installation | Introduction | The problem | What this package provides | Relationship to existing tools | Why Bioconductor? | Quick start | Pipe-friendly workflow | Importing data | From VCF files | From iVar TSV | The WithinHostExperiment object | Quality control | Standard QC with flagISNV | Replicate-aware QC | Primer site filtering | Diversity analysis | Transmission analysis | Shared variant statistics | Quick bottleneck estimation | Interoperability | Convert to data.frame | Convert to VRanges | Export for downstream bottleneck tools | Visualisation | Reporting summaries | Case study: SARS-CoV-2 transmission pair | QC and diversity | Other pathogens | Influenza A (H3N2 HA segment) | HIV-1 (pol region) | Mycobacterium tuberculosis | Neutrality tests across pathogens | Advanced features | Neutrality tests | GFF3 gene annotation | Selection analysis | Within-host dN/dS | Selection coefficients from frequency trajectories | Multi-caller consensus | Exact bottleneck estimation | Longitudinal frequency tracking | SCI strand bias filtering | Adaptive technology-specific filtering | Diversity modelling and statistical utilities | Plausibility check | Scope and limitations | Session info

Installation | Introduction | Match each method to its estimand | Real-data complete-null diagnostics | A complete-null check in miniature | Simulation data-generating mechanisms | Metrics | A power approximation for planning | Comparator alignment | Multiplicity | Reproducibility record | Developmental ERD assessment | Session information

Overview | 1. Forecast accuracy benchmark | Results | Comparison with published benchmarks | 2. Calibration diagnostics | Key finding | Recalibration | Conformal prediction | 3. Sequential detection | Limitation | 4. Fitness decomposition | 5. Multi-pathogen applicability | References

The resource allocation problem | Expected Value of Information | Adaptive allocation via Thompson sampling | Detection horizon | Sequential detection with controlled false alarms | Surveillance dashboard | Comparison with existing approaches | References

The confounding of transmissibility and immune escape | Constructing the immunity landscape | Fitness decomposition | DMS-informed early detection | Selective pressure as an early warning signal | Practical considerations | References

Why calibration matters | PIT diagnostics on real CDC data | Recalibration | Conformal prediction intervals | Adaptive conformal inference | Proper scoring rules | References

Motivation | Example: synthesizing patient records | Parametric synthesis (Gaussian copula) | Validation | Comparing methods | Privacy risk assessment | Downstream model fidelity | Privacy-utility trade-off

Overview | Example: COMPAS recidivism prediction | Create fairness data | Compute fairness metrics | Generate fairness report | Visualize disparities | Group-wise calibration | Threshold optimization | Intersectional analysis

Overview | Load data | Collapse rare lineages | Fit MLR model | Growth advantages | Frequency trajectories | Forecast | Emergence detection | Sequencing power | Session info

Motivation | Data source | Setting up the design | Sequencing inequality | The bias problem: weighted vs naive | Optimal resource allocation | Delay correction and nowcasting | Combined correction | Key takeaways | Reproducibility

The right-truncation problem | Estimating the delay distribution | Reporting probability | Nowcasting | Combined design + delay correction | Choosing a delay distribution

Why allocation matters | The three objectives | Example | Optimize for minimum MSE | Compare all strategies | With minimum coverage constraints | Choosing an objective

The problem | What survinger does | Quick start | Comparing weighted vs naive estimates | Optimizing resource allocation | Delay correction | Combined correction | Detection power | How survinger differs from phylosamp | Next steps

Overview | Setup | Engine 1: MLR | Engine 2: Piantham | Comparing fit statistics | Backtesting | Scoring | Model ranking | Visualization | When to use which engine | Hierarchical MLR

Overview | Preparing data | Fitting a model | Extracting growth advantages | Visualizing the fit | Forecasting | Detecting emerging lineages | Next steps

Overview | Step 1: Load and prepare data | Step 2: Collapse rare lineages | Step 3: Fit model | Step 4: Growth advantages | Step 5: Identify emerging lineages | Step 6: Forecast | Step 7: Assess sequencing needs | Step 8: Extract tidy results | Summary