AME model fitting options — ame

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
)

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.

Value

List of options to pass to ame()

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.

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau

Examples

# \donttest{
# Configure options
opts <- ame_options(verbose = TRUE, odens = 25)
opts
#> $parallel_chains
#> [1] 1
#> 
#> $verbose
#> [1] TRUE
#> 
#> $odens
#> [1] 25
#> 
#> $use_sparse_matrices
#> [1] FALSE
#> 
# }