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)
    <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'.
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: 2026-06-19 07:29:51 UTC; thinkbook-cxy
Author: Xingyu Chen [aut, cre],
  Lin Liu [aut]
Maintainer: Xingyu Chen <xingyuchen0714@sjtu.edu.cn>
Repository: CRAN
Date/Publication: 2026-06-24 08:40:10 UTC
