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  "Title": "A Graph-Based Cross-Fitting Engine in R",
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  "Description": "Provides a general cross-fitting engine for semiparametric\nestimation (e.g., double/debiased machine learning). Supports\nuser-defined target functionals and directed acyclic graphs of\nnuisance learners with per-node training fold widths,\ntarget-specific evaluation windows, and fold-allocation modes\n(\"overlap\", \"disjoint\", \"independence\"). Returns either numeric\nestimates (mode = \"estimate\") or cross-fitted prediction\nfunctions (mode = \"predict\"), with configurable aggregation\nover panels and repetitions, reuse-aware caching, and failure\nisolation, making it well-suited for simulation studies and\nlarge benchmarks.",
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  "URL": "https://github.com/EtiennePeyrot/crossfit-R",
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  "Date/Publication": "2026-03-04 19:45:21 UTC",
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      "title": "crossfit: Cross-Fitting Engine for Double / Debiased Machine Learning",
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        "1. Motivation: why another cross-fitting engine?",
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        "3. A simple regression example",
        "3.1 Single-method cross-fitting with crossfit()",
        "4. Multiple methods and shared nuisances",
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