Display memory usage information for AME models

Shows estimated memory usage for networks of given size. Memory optimization is automatic, so this is informational only.

Usage

ame_memory_settings(n_nodes, R = 2)

Arguments

n_nodes

Number of nodes in network

R

Rank of multiplicative effects (default: 2)

Value

Invisibly returns memory estimates

Details

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

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau