Perform the block fused lasso with thresholding to detect candidate break points.
var.first.step.blocks(
data_y,
lambda1,
lambda2,
q,
max.iteration,
tol,
blocks,
cv.index,
HBIC = FALSE,
gamma.val = NULL
)input data matrix Y, with each column representing the time series component
tuning parmaeter lambda_1 for fused lasso
tuning parmaeter lambda_2 for fused lasso
the AR order
max number of iteration for the fused lasso
tolerance for the fused lasso
the blocks
the index of time points for cross-validation
logical; if TRUE, use high-dimensional BIC, if FALSE, use orginal BIC. Default is FALSE.
hyperparameter for HBIC, if HBIC == TRUE.
A list object, which contains the followings
estimated jump size in L2 norm
estimated jump size in L1 norm
estimated change points in the first step
estimated parameters in the first step