# Usage

    library(SyScSelection)

### Example ellipsodial mesh for a normal distribution:

- 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)`

### Example ellipsodial mesh for a t distribution:

- 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)`