Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.

Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.

Functions used for graphing in multivariate contexts. These 
    functions are designed to support produce reasonable graphs with minimal 
    input of graphing parameters. The motivation for these functions was to 
    support students learning multivariate concepts and R - there may be 
    other functions and packages better-suited to practical data analysis. For
    details about the ellipse methods see Johnson and Wichern (2007, 
    ISBN:9780131877153).

This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

Compute the coefficient of determination for outcomes in n-dimensions. 
  May be useful for multidimensional predictions (such as a multinomial model) or
  calculating goodness of fit from latent variable models such as probabilistic
  topic models like latent Dirichlet allocation or deterministic topic models 
  like latent semantic analysis. Based on Jones (2019) <arXiv:1911.11061>.