A function to plot a regression nomogram of regression 
    objects. Covariate distributions are superimposed on nomogram scales and the plot
	can be animated to allow on-the-fly changes to distribution representation and to 
	enable outcome calculation.

Provides R6 classes, methods and utilities to construct,
    analyze, summarize, and visualize regression models.

Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.

The graphical approach is proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p-values. A reverse graphical approach starts from a set of singleton graphs, and gradually add vertices into graphs until rejection of a set of hypotheses is made. See Gou, J. (2020). Reverse graphical approaches for multiple test procedures. Technical Report.

Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (best
        linear unbiased predictors, BLUPs).
        Clifford and McCullagh (2006)
        <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.

Simulate samples from populations with known covariate
    distributions, generate response variables according to common linear and
    generalized linear model families, draw from sampling distributions of
    regression estimates, and perform visual inference on diagnostics from model
    fits.