lame (version 1.3.4)

ame_options: AME model fitting options

Description

Configure options for fitting AME models. Memory efficiency is now handled automatically based on network size.

Usage

ame_options(
  parallel_chains = 1,
  verbose = TRUE,
  odens = 25,
  use_sparse_matrices = FALSE
)

Value

List of options to pass to ame()

Arguments

parallel_chains

Number of parallel chains to run (default: 1)

verbose

Logical; print progress information (default: TRUE)

odens

Output density - save every odens iterations (default: 25)

use_sparse_matrices

Logical; use sparse matrices for storing results (default: FALSE). Set to TRUE if your network is actually sparse (many zero/NA entries) and memory is a concern.

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau

Details

Memory optimization features:

• Redundant matrices (EZ, UVPM) are never stored - they can be reconstructed if needed

• use_sparse_matrices = TRUE: Converts large matrices to sparse format

• Posterior samples are always thinned appropriately

When to use sparse matrices:

• Your network has < 10\

• Memory usage is a critical concern

• You're willing to trade computational speed for memory efficiency

Note: For dense networks (most edges observed), sparse matrices will be slower and may use more memory than dense storage.

Examples

# \donttest{
# Configure options
opts <- ame_options(verbose = TRUE, odens = 25)
opts
# }