Contribution table for credit assignment based on 'ggplot2'.
    This can improve the author contribution information in academic journals and personal CV.

Solves control systems problems relating to time/frequency response, LTI systems design and analysis, transfer function manipulations, and system conversion.

Inference with control function methods for nonlinear outcome models when the model is known ('Guo and Small' (2016) <arXiv:1602.01051>) and when unknown but semiparametric ('Li and Guo' (2021) <arXiv:2010.09922>).

Nonparametric two-sample procedure for comparing survival quantiles.

Graphically display the (causal) effect of a continuous variable on a time-to-event outcome
	using multiple different types of plots based on g-computation. Those functions
	include, among others, survival area plots, survival contour plots, survival quantile plots and
	3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally.
	For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.

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.