Pre-testing and diagnostic tools for panel data analysis.
    Researchers should run these tests before any panel regression to
    verify modelling assumptions. The package implements: (1) the Hsiao
    (2014, <ISBN:978-1-107-65763-2>) homogeneity F-tests (F1/F2/F3),
    Swamy (1970) <doi:10.2307/1913012> parameter heterogeneity test, and
    Pesaran (2004) <doi:10.2139/ssrn.572504> cross-sectional dependence
    test via xtpretest(); (2) missing-data detection, mechanism testing,
    and imputation for unbalanced panels via xtmispanel(); (3)
    quantile-regression cross-sectional dependence tests (T_tau and
    T-tilde_tau statistics) of Demetrescu, Hosseinkouchack and Rodrigues
    (2023) <doi:10.1016/j.jeconom.2022.09.001> via xtcsdq(); and (4) the
    panel quantile-regression slope homogeneity S-hat and D-hat
    statistics of Galvao, Juhl, Montes-Rojas and Olmo (2017)
    <doi:10.1080/07350015.2015.1054493> via xtqsh(). Together these
    tests address three fundamental pre-testing questions: (i) are
    slopes homogeneous? (ii) is there cross-sectional dependence? and
    (iii) is the panel balanced and is missingness ignorable?

Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) <doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented.

Aligns irregularly timed longitudinal observations to regular panel
    schedules. Each observation on a subject serves as a potential baseline, and
    all later observations are aligned to the nearest panel with minimal delay.
    The package provides tools to: (1) build a panel book documenting alignments
    and delays, (2) organize variables in longitudinal data to wide-format panel
    matrices, (3) summarize alignment quality and panel coverage. Ideal for
    cross-lagged panel analysis, clinical trials with variable visit schedules,
    and Electronic Health Record harmonization.