{
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  "Package": "HOIF",
  "Type": "Package",
  "Title": "Higher-Order Influence Function Estimators for the Average\nTreatment Effect",
  "Version": "0.2.0",
  "Authors@R": "c(person(\"Xingyu\", \"Chen\",\nemail = \"xingyuchen0714@sjtu.edu.cn\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Lin\", \"Liu\",\nemail = \"linliu@sjtu.edu.cn\",\nrole = \"aut\"))",
  "Description": "Implements Higher-Order Influence Function (HOIF)\nestimators of the Average Treatment Effect (ATE), following\nRobins et al. (2008) <doi:10.1214/193940307000000527>, Liu et\nal. (2017) <doi:10.48550/arXiv.1705.07577> and Liu and Li\n(2023) <doi:10.48550/arXiv.2302.08097>. Estimators of any order\nare supported, with optional covariate basis transformations\n(B-splines, Fourier) and optional K-fold sample splitting\n(cross-fitting) for improved finite-sample performance. The\ncore higher-order U-statistics are computed exactly via the\n'ustats' package, an R interface to the 'Python' package\n'u-stats'; the underlying algorithm and its computational\ncomplexity are analyzed in Chen, Zhang and Liu (2025)\n<doi:10.48550/arXiv.2508.12627>. A pure R implementation (up to\norder 6) is also provided as a fallback that does not require\n'Python'.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "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)\nwith the 'u-stats', 'numpy' and 'torch' packages (provisioned\nautomatically on first use via 'reticulate', or via\nustats::setup_ustats()). Not needed when pure_R_code = TRUE.",
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  "Language": "en-US",
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  "Packaged": {
    "Date": "2026-06-24 13:22:05 UTC",
    "User": "root"
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  "Author": "Xingyu Chen [aut, cre], Lin Liu [aut]",
  "Maintainer": "Xingyu Chen <xingyuchen0714@sjtu.edu.cn>",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-06-24 08:40:10 UTC",
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  "_exports": [
    "build_Ej",
    "calculate_u_statistics_pure_r_six",
    "compute_basis_matrix",
    "compute_gram_inverse",
    "compute_hoif_estimators",
    "compute_residuals",
    "hoif_ate",
    "transform_covariates"
  ],
  "_help": [
    {
      "page": "build_Ej",
      "title": "Build E_j tensor structure",
      "topics": [
        "build_Ej"
      ]
    },
    {
      "page": "calculate_u_statistics_pure_r_six",
      "title": "Compute U-statistics HOIF-type from Order 2 to 6 in pure R code.",
      "topics": [
        "calculate_u_statistics_pure_r_six"
      ]
    },
    {
      "page": "compute_basis_matrix",
      "title": "Compute basis projection matrices",
      "topics": [
        "compute_basis_matrix"
      ]
    },
    {
      "page": "compute_gram_inverse",
      "title": "Compute inverse of weighted Gram matrix",
      "topics": [
        "compute_gram_inverse"
      ]
    },
    {
      "page": "compute_hoif_estimators",
      "title": "Compute HOIF Estimators for ATE",
      "topics": [
        "compute_hoif_estimators"
      ]
    },
    {
      "page": "compute_residuals",
      "title": "Compute residuals for both treatment groups",
      "topics": [
        "compute_residuals"
      ]
    },
    {
      "page": "hoif_ate",
      "title": "Main function: HOIF estimators for ATE with optional sample splitting",
      "topics": [
        "hoif_ate"
      ]
    },
    {
      "page": "plot.hoif_ate",
      "title": "Plot convergence of ATE estimates",
      "topics": [
        "plot.hoif_ate"
      ]
    },
    {
      "page": "print.hoif_ate",
      "title": "Print method for hoif_ate objects",
      "topics": [
        "print.hoif_ate"
      ]
    },
    {
      "page": "transform_covariates",
      "title": "HOIF Estimators for Average Treatment Effect",
      "topics": [
        "transform_covariates"
      ]
    }
  ],
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      "source": "hoif-vignette.Rmd",
      "filename": "hoif-vignette.html",
      "title": "Introduction to HOIF: Higher-Order Influence Function Estimators for the ATE",
      "author": "Xingyu Chen",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Background",
        "Key Features",
        "Mathematical Background",
        "The HOIF Framework",
        "U-Statistics Formulation",
        "Sample Splitting",
        "Installation",
        "Setting up the Python backend",
        "No Python at all?",
        "Quick Start Example",
        "Generate Simulated Data",
        "Split the Sample",
        "Estimate (Misspecified) Nuisance Functions on the Nuisance Sample",
        "The First-Order AIPW Estimator and its Bias",
        "Compute the eHOIF Estimator (with sample splitting)",
        "Compute the sHOIF Estimator (without sample splitting)",
        "Debias the AIPW Estimator",
        "Visualize Convergence",
        "Main Function: hoif_ate()",
        "Arguments",
        "Return Value",
        "Advanced Usage",
        "Using Basis Expansion",
        "Sample Splitting (Cross-Fitting)",
        "Regularized Gram Matrix Inversion",
        "Pure R Backend",
        "Computational Details",
        "Python Backend (ustats)",
        "Pure R Implementation",
        "Performance Considerations",
        "Practical Recommendations",
        "Choosing the Transformation Method",
        "Choosing the Order",
        "Use a GPU if Available",
        "Troubleshooting",
        "Python Backend Issues",
        "Numerical Instability",
        "Extreme Propensity Scores",
        "References"
      ],
      "created": "2026-06-24 08:40:10",
      "modified": "2026-06-24 08:40:10",
      "commits": 1
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