How to impose long run restriction on an SVAR model in svars package

Hi everyone,

I'm using the svars package to build an SVAR model. I'm curious to find out how to impose long-run restrictions on the svar object that I have in my example below (similar to the seminal Blanchard and Quah paper - https://uh.edu/~bsorense/BlanchardQuah1989.pdf). I essentially would like to restrict the long-run impact of GDP's response from a CPI shock to zero.

In the vars package, I know that this could be done like this (svar_object <- BQ(var_object)),
however I'm unsure of how to achieve a similar effect within the svars package. Is there perhaps a way in which I could pass a restriction matrix as an argument within the code I've included below to do this? Or any other suggestions are also welcome!

TIA!

# Load Packages
library(tidyverse)
library(svars)

# Load snippet of dataset 

data <- structure(c(-1.87308398729904, 2.290631067509, 0.993462708465565, 
-1.29041209029221, 1.12545485962681, -2.636526517961, 1.64791593479841, 
-0.127800586695059, -0.649131035554795, -1.41838122120497, -0.0727559182898574, 
2.61173864035165, 0.408195006166245, 2.14851460203356, -3.43206431417578, 
-3.21097438990066, -0.633222142832857, 3.62032490350241, -1.88466472673507, 
3.1882672460176, -6.25889275693812, -5.63387155573629, -2.47686181080518, 
-0.00946230989793406, 1.15917422619178, 1.26429447879497, 0.986332225049047, 
2.41168238340004, 1.84115722165812, 1.41639006961984, 1.35342674612451, 
2.79543996486087, 0.605266622518563, 0.554959915380682, 1.07421168196309, 
1.2403520539725, 0.862800885835391, 1.26464488451186, 1.75852063191533, 
0.546966935763926, 0.534163056633208, 0.63109068920264, 0.935286647736344, 
0.159975810472712, 0.208743967794067, 0.973862299523898), .Dim = c(23L, 
2L), .Dimnames = list(NULL, c("GDP", "CPI")), index = structure(c(1420070400, 
1427846400, 1435708800, 1443657600, 1451606400, 1459468800, 1467331200, 
1475280000, 1483228800, 1491004800, 1498867200, 1506816000, 1514764800, 
1522540800, 1530403200, 1538352000, 1546300800, 1554076800, 1561939200, 
1569888000, 1577836800, 1585699200, 1593561600), tzone = "UTC", tclass = "Date"), class = c("xts", 
"zoo"))

#  Create VAR model
var.model <- data %>% VAR(.,p=1,type = 'cons',season=NULL)

# Create SVAR model by Cholesky Decomposition

svar.model <- id.chol(var.model, order_k = c('GDP','CPI'))