Usage

Estimate the mean and covariance matrix from the data:
mu <- colMeans(data)
sig <- cov(data)

The number of dimensions, d, is taken directly from the data:
d <- length(data[1,])

Get the size parameter for a normal dist’n at a 95% threshold:
calpha <- sizeparam_normal_distn(.95, d)

Create a hyperellipsoid object. Note that the constructor takes the inverse of the disperion matrix:
hellip <- hyperellipsoid(mu, solve(sig), calpha)

Scenarios are calculated as a mesh of fineness 3. The number of scenarios is a function of the dimensionality of the hyperellipsoid and the fineness of the mesh:
scenarios <- hypercube_mesh(3, hellip)

Estimate the mean, covariance, and degrees of freedom from the data:
mu <- colMeans(data)
sig <- cov(data)
nu <- dim(data)[1] - 1

The number of dimensions, d, is taken directly from the data:
d <- length(data[1,])

Get the size parameter for a normal dist’n at a 95% threshold:
calpha <- sizeparam_t_distn(.95, d, nu)

Create a hyperellipsoid object. Note that the constructor takes the inverse of the disperion matrix:
hellip <- hyperellipsoid(mu, solve(sig), calpha)

Scenarios are calculated as a mesh of fineness 3. The number of scenarios is a function of the dimensionality of the hyperellipsoid and the fineness of the mesh:
scenarios <- hypercube_mesh(3, hellip)