Bagging or Random Subspace Method for Random Forest?
Most modern implementations do both, at least optionally.

sklearn has max_features and bootstrap.
ranger has mtry and replace/sample.fraction.
xgboost's random forest has colsample_bynode and subsample.
h2o has mtries/col_sample_rate_per_tree and sample_rate (and a couple modifiers).