The data | Looking at one frame | Writing the animation | Arguments worth knowing | Coloring by group | Images on the bars | References

Values move at a constant rate | Rank does not move at a constant rate | Ranks are clamped just below the visible window | The geometry never moves | What a frame is made of | Cost

Choosing a method for single-arm indirect comparisons: ML-UMR, STC, and naive | A shared dataset | Naive and STC | ML-UMR (SPFA and relaxed) | A comparison table | Interpreting the differences | Model comparison | Decision guide | References

What mlumr does | Supported outcome families | The four methods | What "unanchored" does and does not buy you | Installation | The workflow at a glance | Where to go next

The pipeline (illustrated on a binary endpoint) | Outcome-family input reference | Covariate naming and types | Scale conventions for aggregate summaries | Integration points | Correlation handling | Inspecting the integration points | Visualizing the integration points | Integration requirements for STC and naive

Aggregate subgroup information for the relaxed model | What an aggregate row contributes | Rows must describe one population | Mean-profile geometry | Using check_identification() | Recommended workflow | Distribution and dependence assumptions | Time-to-event outcomes | Simulation evidence | What this vignette does not claim | References

Binary outcomes: an unanchored PASI 75 comparison | The clinical question | The ML-UMR model | Covariate balance | Setting up the ML-UMR data | Frequentist benchmarks | Fitting the ML-UMR models | Treatment effects | Absolute predictions | Conditional effects | Model comparison | Interpretation | References

Continuous outcomes: an unanchored mean-difference comparison | The clinical question | The ML-UMR model | Covariate balance | Setting up the ML-UMR data | Frequentist benchmarks | Fitting the ML-UMR models | Treatment effects | Absolute predictions | Conditional effects | Model comparison | Interpretation | References

Count outcomes: an unanchored rate-ratio comparison | The clinical question | The ML-UMR model | Covariate balance | Setting up the ML-UMR data | Frequentist benchmarks | Fitting the ML-UMR models | Treatment effects | Absolute predictions | Conditional effects | Model comparison | Interpretation | References

A model to work with | Controlling the sampler | Backend selection | Priors | Inspecting a fit | MCMC diagnostics | Prior sensitivity | Troubleshooting | References

The unanchored survival problem | The ML-UMR survival model | Survival distributions | Censoring | The data: progression-free survival in NDMM | Covariate balance | Setting up the ML-UMR data | Frequentist benchmarks | Fitting the ML-UMR models | Treatment effects | Absolute predictions | Effect modification (relaxed M-spline model) | Model comparison | Log hazard ratios against the naive estimate | Interpretation | References