MetaHunt: Privacy-Preserving Meta-Analysis via Low-Rank Basis Hunting

Tools for privacy-preserving meta-analysis of function-valued quantities (e.g. regression, conditional average treatment effect functions) across heterogeneous studies. Implements the MetaHunt pipeline of Shi, Imai, and Zhang: denoised functional Successive Projection Algorithm (d-fSPA) for basis hunting, constrained projection of study functions onto the recovered simplex, Dirichlet regression of mixing weights on study-level covariates, target prediction, and split or cross conformal prediction intervals.

Main entry points

• metahunt() — fit the full pipeline end-to-end.

• predict.metahunt() — predict target functions for new study-level covariates.

• split_conformal(), cross_conformal(), conformal_from_fit() — prediction intervals.

• minmax_regret() — covariate-free worst-case-regret aggregator (Zhang, Huang, and Imai 2024) as a baseline.

• f_hat_from_models(), build_grid() — onramp from fitted-model lists and reference data to the package's matrix inputs.

Pipeline building blocks

dfspa(), project_to_simplex(), fit_weight_model(), predict_target(), apply_wrapper(), reconstruction_error_curve(), cv_error_curve(), select_denoising_params().

See also

Useful links:

• https://github.com/WShi18/MetaHunt

• https://wshi18.github.io/MetaHunt/

• https://arxiv.org/abs/2604.23847

• Report bugs at https://github.com/WShi18/MetaHunt/issues

Author

Maintainer: Wenqi Shi wenqishi18@gmail.com

Authors:

• Kosuke Imai imai@harvard.edu

• Yi Zhang yizhang0017@gmail.com