A Bayesian approach to using 
    predictive probability in an ANOVA construct with a continuous normal response, 
    when threshold values must be obtained for the question of interest to be 
    evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). 
    The Bayesian Mission Mean (BMM) is used to evaluate a question 
    of interest (that is, a mean that randomly selects combination of factor levels 
    based on their probability of occurring instead of averaging over the factor 
    levels, as in the grand mean). Under this construct, in contrast to a Gibbs 
    sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is 
    required. The nested sampler determines the conditional posterior distribution 
    of the model parameters, given Y, and the outside sampler determines the marginal 
    posterior distribution of Y (also commonly called the predictive distribution for Y). 
    This approach provides a sample from the joint posterior distribution of Y and 
    the model parameters, while also accounting for the threshold value that must be 
    obtained in order for the question of interest to be evaluated as successful.

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>.