Package: HOIF Type: Package Title: Higher-Order Influence Function Estimators for the Average Treatment Effect Version: 0.2.0 Authors@R: c(person("Xingyu", "Chen", email = "xingyuchen0714@sjtu.edu.cn", role = c("aut", "cre")), person("Lin", "Liu", email = "linliu@sjtu.edu.cn", role = "aut")) Description: Implements Higher-Order Influence Function (HOIF) estimators of the Average Treatment Effect (ATE), following Robins et al. (2008) , Liu et al. (2017) and Liu and Li (2023) . 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) . A pure R implementation (up to order 6) is also provided as a fallback that does not require 'Python'. License: MIT + file LICENSE Encoding: UTF-8 Depends: R (>= 4.0.0) Imports: splines, corpcor, SMUT, ustats (>= 0.1.5) Suggests: MASS, testthat (>= 3.0.0), reticulate, knitr, rmarkdown URL: https://cxy0714.github.io/HOIF/, https://github.com/cxy0714/HOIF BugReports: https://github.com/cxy0714/HOIF/issues 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. Config/testthat/edition: 3 RoxygenNote: 7.3.3 VignetteBuilder: knitr Language: en-US NeedsCompilation: no Packaged: 2026-06-24 13:22:05 UTC; root Author: Xingyu Chen [aut, cre], Lin Liu [aut] Maintainer: Xingyu Chen Config/pak/sysreqs: python3 Repository: https://cran.r-universe.dev Date/Publication: 2026-06-24 08:40:10 UTC RemoteUrl: https://github.com/cran/HOIF RemoteRef: HEAD RemoteSha: c28d25f48384cf893a0653da1cca0bd86fb67eb3