Changes in version 0.1.0 (2026-05-12)                  

Initial public release.

End-to-end fitter

  - metahunt() chains denoised functional SPA basis hunting, constrained
    simplex projection, and Dirichlet weight modelling in a single call.
    Method dispatch for predict(), summary(), and plot() on the returned
    "metahunt" object.

Conformal prediction

  - split_conformal() and cross_conformal() return distribution-free
    prediction intervals around the target function (pointwise on the
    grid or, with a wrapper, around a scalar summary).
  - conformal_from_fit() adds intervals to an already-fit pipeline using
    a held-out calibration set.
  - coverage(), summary(), and plot() methods for the
    "metahunt_conformal" class.

Rank and tuning selection

  - reconstruction_error_curve() (unsupervised elbow) and
    cv_error_curve() (supervised CV) for picking K.
  - select_denoising_params() cross-validates the (N, Delta) knobs of
    dfspa().

Pipeline building blocks

  - dfspa() denoised functional Successive Projection Algorithm
    (Algorithm 1 of the paper).
  - project_to_simplex() constrained simplex projection of each study's
    function onto the recovered bases (quadratic program via quadprog).
  - fit_weight_model() and predict.metahunt_weight_model() for Dirichlet
    regression of simplex weights on study-level covariates, with
    coef.metahunt_weight_model() for inspecting coefficients.
  - predict_target() and apply_wrapper() for composing predictions and
    scalar summaries by hand.

Data preparation

  - build_grid() constructs a shared evaluation grid from any reference
    patient-level dataset.
  - f_hat_from_models() evaluates a list of fitted models on the shared
    grid with class-aware dispatch for ranger, grf (causal_forest,
    regression_forest), and a default branch that covers
    lm/glm/randomForest. Custom S4 classes can supply their own
    predict_fn.

Baselines

  - minmax_regret() implements the covariate-free worst-case-regret
    aggregator of Zhang, Huang, and Imai (2024, arXiv:2412.11136).

Documentation

  - Tutorials: metahunt-intro, data-prep, grid-weights, wrapper-scalar,
    plus get-started.
  - Companion paper: Shi, Imai, and Zhang (2024, arXiv:2604.23847).