The problem: function-valued meta-analysis | Key assumptions | A1. Low-rank cross-study heterogeneity | A2. Weight model | A3. Exchangeability | A4. Estimation error control | The three-step pipeline | Step 1. Basis hunting via d-fSPA | Step 2. Fitting weight model | Step 3. Target prediction | A worked end-to-end example | Where to next | References

What grid_weights does | When the default is fine | When non-uniform grid_weights matter | A worked comparison: uniform vs non-uniform | Practical guidance | Functions that accept grid_weights

Why this matters | A small standalone simulation | Unsupervised diagnostic: reconstruction error vs K | Supervised diagnostic: cross-validated prediction error vs K | The d-fSPA denoising knobs (N, Delta) | Bypassing denoising | Setting (N, Delta) by hand | Tuning (N, Delta) by CV | Practical recipe | See also

Why conformal prediction here | A small standalone simulation | The three flavours | Split conformal | Cross conformal | Pre-fit conformal | Pointwise vs scalar bands | Small-m warning on cal_frac | See also

What this is | Inputs in 30 seconds | A minimal end-to-end run | Visualising the result | Where to next

Why this step matters | build_grid() | f_hat_from_models() | Dispatch table | ranger example (not run) | grf::causal_forest example (not run) | Worked lm example | predict_fn for custom S4 / bespoke classes | Multi-dimensional grids (3 covariates) | Sanity checks | Common pitfalls

Why scalar summaries | The wrapper protocol | An ATE example with grf::causal_forest | Three custom wrappers | Plain mean | Restricted positive mean | Endpoint contrast | Conformal coverage with a wrapper | Pointwise vs scalar — quick reference | See also

What minmax_regret() does | When to use which | A small standalone simulation | Running minmax_regret() | Visual comparison: minmax_regret vs metahunt | Small-m users | See also