Estimates heterogeneous coefficient models for large panels
    with cross-sectional dependence. Implements the Mean Group (MG)
    estimator of Pesaran and Smith (1995)
    <doi:10.1016/0304-4076(94)01644-F>, the Common Correlated Effects
    (CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006)
    <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran
    (2015) <doi:10.1016/j.jeconom.2015.03.007>, the regularized CCE
    of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and
    Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai
    (2009) <doi:10.3982/ECTA6135>, and long-run estimators including
    Cross-Sectionally augmented Distributed Lag (CS-DL),
    Cross-Sectionally augmented Autoregressive Distributed Lag
    (CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin
    et al. 1999). Also provides rolling-window estimation,
    high-dimensional fixed effect absorption, spatial CCE via
    user-supplied weight matrices, and structural break tests (Chow
    and sup-Wald) following Andrews (1993), Bai and Perron (1998),
    and Ditzen, Karavias and Westerlund (2024). Supplies a
    comprehensive cross-sectional dependence (CD) test suite including
    the Pesaran (2015) CD test <doi:10.1080/07474938.2014.956623>,
    the Juodis and Reese (2022) randomized weighted CD (CDw) test,
    the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test,
    the Fan et al. (2015) Power Enhancement Approach (PEA) test, and
    the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further
    diagnostics include the Pesaran (2007) Cross-sectionally
    Augmented IPS (CIPS) panel unit root test
    <doi:10.1002/jae.951>, the Westerlund (2007) panel cointegration
    tests, the Dumitrescu and Hurlin (2012) panel Granger causality
    test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel
    unit root tests, the Pedroni (2004) and Kao (1999) residual
    cointegration tests, the Swamy (1970) and Pesaran and Yamagata
    (2008) slope homogeneity tests, a Hausman-type test for MG versus
    pooled, the exponent of cross-sectional dependence from Bailey et
    al. (2016) <doi:10.1002/jae.2490>, information criteria for
    Cross-Sectional Average (CSA) selection, the rank condition
    classifier, impulse response functions, cross-section and wild
    bootstrap inference, and 'broom'-compatible methods.