An R6 class "Replacer" provided by the package simplifies working with
    regex patterns containing named groups. It allows easy retrieval of matched portions
    and targeted replacements by group name, improving both code clarity and maintainability.

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>.

Plot regression surfaces and marginal effects in three dimensions. The plots are 'plotly' objects and can be customized using functions and arguments from the 'plotly' package.

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.

The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.