Initial public release.
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.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.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().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.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.minmax_regret() implements the covariate-free worst-case-regret
aggregator of Zhang, Huang, and Imai (2024,
arXiv:2412.11136).metahunt-intro, data-prep, grid-weights,
wrapper-scalar, plus get-started.