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

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

Version:	0.1.5
Depends:	R (≥ 3.5.0)
Imports:	R6 (≥ 2.4.1), data.table (≥ 1.12.8), mlr3 (≥ 0.5.0), mlr3tuning (≥ 0.3.0), mlr3learners (≥ 0.3.0), mlr3misc, mvtnorm, utils, clusterGeneration, readstata13, magrittr, dplyr, stats, MLmetrics, checkmate
Suggests:	rpart, mlr3pipelines
Published:	2025-09-08
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

Documentation:

Reference manual:

xtdml.html , xtdml.pdf

Downloads:

Package source:	xtdml_0.1.5.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): xtdml_0.1.5.tgz, r-oldrel (arm64): xtdml_0.1.5.tgz, r-release (x86_64): xtdml_0.1.5.tgz, r-oldrel (x86_64): xtdml_0.1.5.tgz

Linking:

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