crossfit: A Graph-Based Cross-Fitting Engine in R

Abstract

Cross-fitting is a key ingredient in many semiparametric estimation procedures, such as double/debiased machine learning (DML), enabling valid estimation of low-dimensional targets in the presence of high-dimensional nuisance functions by enforcing out-of-sample use of nuisance predictions. crossfit is an R package that provides a general-purpose, estimator-agnostic cross-fitting engine. Users specify (i) a target functional and (ii) a directed acyclic graph (DAG) of nuisance models, with node-specific training fold widths and target-specific evaluation windows. The engine executes a reproducible schedule over folds, panels, and repetitions, returning either a scalar estimate (mode="estimate") or a cross-fitted predictor function for application to new data (mode="predict"). Beyond standard cross-fitting, crossfit implements fold-allocation modes that control how training data are shared across nuisance components, including disjoint and independence-enforcing allocations that duplicate reused nodes to reduce dependence between nuisance branches. The implementation targets simulation-heavy benchmarking and method development, with explicit and auditable schedules, defensive validation of specifications and nuisance dependencies, reuse-aware caching to avoid redundant refits, and failure isolation policies for large experiment grids. The crossfit package is available on CRAN, openly developed on GitHub under GPL-3, and is intended as a lightweight, tested foundation to prototype and empirically evaluate cross-fitted estimators with explicit control over fold geometry, dependence, and computation.