xtdml: Double Machine Learning for Static Panel Models with Fixed Effects

The 'xtdml' package implements partially linear panel regression (PLPR) models with high-dimensional confounding variables and an exogenous treatment variable within the double machine learning framework. The package is used to estimate the structural parameter (treatment effect) in static panel data models with fixed effects using the approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. 'xtdml' is built on the object-oriented package 'DoubleML' (Bach et al., 2024) <doi:10.18637/jss.v108.i03> using the 'mlr3' ecosystem.

Version:	0.1.12
Depends:	R (≥ 3.5.0)
Imports:	R6 (≥ 2.4.1), data.table (≥ 1.12.8), mlr3 (≥ 1.3.0), mlr3tuning (≥ 1.5.0), mlr3learners (≥ 0.13.0), mlr3misc (≥ 0.19.0), mvtnorm, utils, clusterGeneration, readstata13, magrittr, dplyr, stats, MLmetrics, checkmate, rlang
Suggests:	rpart, mlr3pipelines, bbotk (≥ 1.8.0)
Published:	2026-03-13
DOI:	10.32614/CRAN.package.xtdml
Author:	Annalivia Polselli [aut, cre]
Maintainer:	Annalivia Polselli <apolselli.econ at gmail.com>
License:	GPL-2 | GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	xtdml results

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