HOIF: Higher-Order Influence Function Estimators for the Average
Treatment Effect
Implements Higher-Order Influence Function (HOIF) estimators
of the Average Treatment Effect (ATE), following Robins et al. (2008)
<doi:10.1214/193940307000000527>, Liu et al. (2017)
<doi:10.48550/arXiv.1705.07577> and Liu and Li (2023)
<doi:10.48550/arXiv.2302.08097>. Estimators of any order are supported,
with optional covariate basis transformations (B-splines, Fourier) and
optional K-fold sample splitting (cross-fitting) for improved
finite-sample performance. The core higher-order U-statistics are
computed exactly via the 'ustats' package, an R interface to the
'Python' package 'u-stats'; the underlying algorithm and its
computational complexity are analyzed in Chen, Zhang and Liu (2025)
<doi:10.48550/arXiv.2508.12627>. A pure R implementation (up to order
6) is also provided as a fallback that does not require 'Python'.
| Version: |
0.2.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
splines, corpcor, SMUT, ustats (≥ 0.1.5) |
| Suggests: |
MASS, testthat (≥ 3.0.0), reticulate, knitr, rmarkdown |
| Published: |
2026-06-24 |
| DOI: |
10.32614/CRAN.package.HOIF (may not be active yet) |
| Author: |
Xingyu Chen [aut, cre],
Lin Liu [aut] |
| Maintainer: |
Xingyu Chen <xingyuchen0714 at sjtu.edu.cn> |
| BugReports: |
https://github.com/cxy0714/HOIF/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://cxy0714.github.io/HOIF/, https://github.com/cxy0714/HOIF |
| NeedsCompilation: |
no |
| SystemRequirements: |
For the default Python backend: Python (>= 3.11)
with the 'u-stats', 'numpy' and 'torch' packages (provisioned
automatically on first use via 'reticulate', or via
ustats::setup_ustats()). Not needed when pure_R_code = TRUE. |
| Language: |
en-US |
| Materials: |
NEWS |
| CRAN checks: |
HOIF results |
Documentation:
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