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.

Implements fixed effects estimators for time-invariant variables
    in panel data models. Provides three estimation methods: FEVD (Fixed Effects
    Vector Decomposition) from Plumper and Troeger (2007) 
    <doi:10.1093/pan/mpm002>, and FEF (Fixed Effects Filtered) and FEF-IV
    (instrumental variables variant) from Pesaran and Zhou (2018)
    <doi:10.1080/07474938.2016.1222225>. All methods use the correct
    Pesaran-Zhou variance estimators that account for generated regressor
    uncertainty, avoiding the size distortions documented in the literature.

Implements the interactive fixed effects ('IFE') panel estimator of
    Bai (2009) <doi:10.3982/ECTA6135> for balanced and unbalanced panels, with
    optional additive unit and/or time fixed effects. Provides analytical
    standard errors ('homoskedastic', 'HC1' heteroskedasticity-robust,
    cluster-robust by unit, and heteroskedasticity- and autocorrelation-
    consistent), together with asymptotic incidental-parameter bias correction
    for large panels, including a dynamic extension for predetermined
    (lagged-dependent) regressors following Moon and Weidner (2017)
    <doi:10.1017/S0266466615000328>. The number of factors is chosen by
    information criteria (Bai and Ng 2002 <doi:10.1111/1468-0262.00273>) or by
    singular value thresholding. Unbalanced panels are handled by an
    expectation-maximisation algorithm with nuclear-norm-regularised
    initialisation, with estimation, analytical inference, and bias correction
    following Su, Wang and Wang (2025) <doi:10.2139/ssrn.5177283> and building
    on the matrix-completion and missing-data factor analysis of Bai and Ng
    (2021) <doi:10.1080/01621459.2021.1967163>. All computations use base R
    only, with no external dependencies.