Estimation of Panel Quantile Autoregressive Distributed Lag 
    (PQARDL) models that combine panel ARDL methodology with quantile 
    regression. Supports Pooled Mean Group (PMG), Mean Group (MG), and 
    Dynamic Fixed Effects (DFE) estimators across multiple quantiles. 
    Computes long-run cointegrating parameters, error correction term speed 
    of adjustment, half-life of adjustment, and performs Wald tests for 
    parameter equality across quantiles. Based on the econometric frameworks 
    of Pesaran, Shin, and Smith (1999) <doi:10.1080/01621459.1999.10474156>, 
    Cho, Kim, and Shin (2015) <doi:10.1016/j.jeconom.2015.02.030>, and 
    Bildirici and Kayikci (2022) <doi:10.1016/j.energy.2022.124303>.

There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>.

Implements the recursively detrended panel unit root tests proposed
    by Westerlund (2015) <doi:10.1016/j.jeconom.2014.09.013>. Two variants are
    provided: the basic t-REC test assuming iid errors, and the robust t-RREC
    test that accounts for serial correlation, cross-sectional dependence, and
    heteroskedasticity via defactoring and BIC-selected lag augmentation. Both
    tests have a standard normal null distribution requiring no mean or variance
    correction. The panel must be strongly balanced.

Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.

Based on 'STATA' xtsum command, it is used to compute summary statistics for a panel data set. It generates overall, between-group, and within-group statistics for specified variables in a panel data set, as presented in S. Porter (2023) <https://stephenporter.org/files/xtsum_handout.pdf>, StataCorp (2023) <https://www.stata.com/manuals/xtxtsum.pdf>.