Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).

This is a pedagogical package, designed to help students understanding convergence of
             random variables. It provides a way to investigate interactively various modes of
	     convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d.
	     random variables. Visualisation of simulated sample paths is possible through interactive
	     plots. The approach is illustrated by examples and exercises through the function
	     'investigate', as described in
	     Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>.
	     The user can study his/her own sequences of random variables.

Tests convergence in macro-financial panels combining
    Dynamic Factor Models (DFM) and mean-reverting, discrete-time
    Ornstein-Uhlenbeck/AR(1) factor processes. Provides: (i) static factor
    extraction with VAR stability checks, Portmanteau tests and rolling
    out-of-sample R^2, in the spirit of Stock and Watson (2002)
    <doi:10.1198/073500102317351921> and the Generalized Dynamic Factor Model of
    Forni, Hallin, Lippi and Reichlin (2000) <doi:10.1162/003465300559037>;
    (ii) cointegration analysis a la Johansen (1988)
    <doi:10.1016/0165-1889(88)90041-3>; (iii) Bayesian factor-OU/AR(1) estimation
    with convergence and half-life summaries grounded in Uhlenbeck and Ornstein
    (1930) <doi:10.1103/PhysRev.36.823> and Vasicek (1977)
    <doi:10.1016/0304-405X(77)90016-2>, with full Markov chain Monte Carlo
    convergence diagnostics; (iv) heteroskedasticity-consistent (HC) and, when
    the suggested 'sandwich' (Zeileis (2004) <doi:10.18637/jss.v011.i10>) and
    'lmtest' packages are available, heteroskedasticity- and autocorrelation-
    consistent (HAC) robust inference, with a self-contained HC fallback;
    (v) coupling significance tests based on time-shift / block-bootstrap nulls
    that preserve marginal dynamics while breaking cross-series dependence; and
    (vi) optional PLS-based factor preselection (Mevik and Wehrens (2007)
    <doi:10.18637/jss.v018.i02>). Functions emphasize reproducibility (explicit
    seeds throughout) and clear, publication-ready summaries.