Shows estimated memory usage for networks of given size. Memory optimization is automatic, so this is informational only.
ame_memory_settings(n_nodes, R = 2)Invisibly returns memory estimates
Number of nodes in network
Rank of multiplicative effects (default: 2)
Cassy Dorff, Shahryar Minhas, Tosin Salau
Memory levers available in the package:
Run compact_ame() on a fitted model to drop empty slots and,
for genuinely sparse posterior means, use sparse storage via
use_sparse_matrices = TRUE
Increase odens in ame()/lame() to store fewer
posterior draws
Pass posterior_opts = list(thin_UV = ..., thin_ab = ...) to thin
the stored latent-factor and additive-effect draws