readBin from memory, rather than the disk - r

Question

I've been working a bit lately with xgboost models:
library(xgboost)
set.seed(23)
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(
data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
pred <- predict(bst, test$data)
Unfortunately, if you try to save and load and xgboost model, the result is a segfault:
f <- tempfile()
saveRDS(bst, f)
bst_new = readRDS(f)
unlink(f)
print(bst_new) #NULL pointer
pred2 <- predict(bst_new, test$data) #This line segfaults
This is because an xgboost model is actually just a pointer to a C++ object in memory: the R package is really just a thin wrapper around some C++ code:
> print(bst)
<pointer: 0x10a5ae750>
attr(,"class")
[1] "xgb.Booster"
The package authors are aware of this, and have written 2 custom functions (xgb.save and xgb.load) for saving and loading xgboost models, but they aren't as useful as just being able to use save and load from base R. I've come up with a hack for saving the model as part of an R session, but it's not very pretty:
#Hack to save an xgboost model
f1 <- tempfile()
f2 <- tempfile()
xgb.save(bst, f1)
model_raw_bytes <- readBin(f1, what='raw', file.info(f)[1, "size"])
unlink(f1)
print(head(model_raw_bytes))
saveRDS(model_raw_bytes, f2)
#Hack to load an xgboost
f3 <- tempfile()
model_raw_bytes <- readRDS(f2)
writeBin(model_raw_bytes, f3)
model_2 <- xgb.load(f3)
pred_2 <- predict(model_2, test$data)
all.equal(pred_2, pred)
unlink(f2)
unlink(f3)
Is there a way to readBin directly from a location in memory, rather than from the disk? (This would save me from writing temp files to save and load the models).
Better yet, is there an accepted way for wrapping C++ objects inside R objects that I can guide the package authors towards? They've been pretty receptive to my comments on github and if I could submit a PR to make this work, I think they'd accept it.