vectorisation: Vectorisation utitilies
In finnlindgren/multiprobit: Bayesian Multivariate Probit Regression

Description Usage Arguments Value Examples

Convert between matrix and vector representations

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cvec(A, d = NULL, sparse = FALSE)

rvec(A, d = NULL, sparse = FALSE)

`A`	A matrix or a vector
`d`	The number of matrix columns
`sparse`	logical; If `TRUE`, use `Matrix::Matrix` to construct matrix output, that detects sparsity. Default: `FALSE`

If A is a matrix, cvec returns the columnwise vectorisation of A. and rvec returns the rowwise vectorisation of A. If A is a vector, cvec and rvec return a matrix with d columns, filled columnwise for cvec and rowwise for rvec.

if (interactive()) {
  Ac <- cvec(1:6, 2) # As matrix(1:6, 3, 2, byrow = FALSE)
  Ar <- rvec(1:6, 2) # As matrix(1:6, 3, 2, byrow = TRUE)
  cvec(Ac) # As as.vector(Ac)
  rvec(Ar) # As as.vector(t(Ar))
}