Implements the semiparametric efficient estimators of 
    continuous-time causal models for time-varying treatments and confounders 
    in the presence of dependent censoring (including structural failure time 
    model and Cox proportional hazards marginal structural model). 
    S. Yang, K. Pieper, and F. Cools (2019) <doi:10.1111/biom.12845>.

Convolute probabilistic distributions using the random generator 
 function of each distribution. A new random number generator function is created that 
 perform the mathematical operation on the individual random samples from the 
 random generator function of each distribution. See the documentation for examples.

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 Ornstein-Uhlenbeck (OU)
    processes. Provides: (i) static/approximate DFMs for large panels with
    VAR/VECM stability checks, Portmanteau tests and rolling out-of-sample R^2,
    following 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 à la Johansen
    (1988) <doi:10.1016/0165-1889(88)90041-3>; (iii) OU-based 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>;
    (iv) robust inference via 'sandwich' HC/HAC estimators (Zeileis (2004)
    <doi:10.18637/jss.v011.i10>) and regression diagnostics ('lmtest'); and
    (v) optional PLS-based factor preselection (Mevik and Wehrens (2007)
    <doi:10.18637/jss.v018.i02>). Functions emphasize reproducibility and clear,
    publication-ready summaries.