create_rand_coef_mat: Creates a random coefficient matrix
In YiHou98/bigtime: Sparse Estimation of Large Time Series Models

Creates a random coefficient matrix

create_rand_coef_mat(
  k,
  p,
  dist = runif,
  max_abs_eigval = 0.8,
  sparsity_pattern = c("none", "lasso", "hvar"),
  sparsity_options = NULL,
  decay = 0.5,
  ...
)

`k`	number of time series
`p`	number of lags
`dist`	distribution to draw coefficients from; must take n as argument indicating number of draws wanted and must return one value per draw wanted, e.g. no vector/matrix returns. Default is uniform distribution: Not currently used
`max_abs_eigval`	if < 1, then var will be stable
`sparsity_pattern`	The sparsity pattern that should be simulated. Options are: none for a dense VAR, lasso for a VAR with random zeroes, and HVAR for an elementwise hirichical sparsity pattern
`sparsity_options`	Named list of additional options for when sparsity pattern is lasso or hvar. For lasso the option num_zero determines the number of zeros. For hvar, the options zero_min (zero_max) give the minimum (maximum) of zeroes for each variable in each equation, and the option zeroes_in_self (boolean) determines if any of the cofficients of a variable on itself should be zero.
`decay`	How fast should coefficient shrink when the lag increases.
`...`	additional arguments forwarded to dist