Unified Interface for Influence Calculation

Provides a unified interface that can use either the Python or R implementation of influence calculation.

Usage

influence_calculator(
  edgelist_simple = NULL,
  meta = NULL,
  filename = NULL,
  sqlite = FALSE,
  method = "r",
  signed = FALSE,
  count_thresh = 3,
  const = 24,
  inhibitory_nts = NULL,
  excluded_nts = NULL,
  lambda_max = 0.99,
  syn_weight_measure = c("norm", "count")
)

Arguments

edgelist_simple

Data frame with edge list. See influence_calculator_r() for column requirements.

meta

Data frame with metadata. See influence_calculator_r() for column requirements.

filename

Character. Path to a connectome data file. SQLite is the legacy format; the Python backend additionally supports CSV / Parquet / Feather detected by extension.

sqlite

Logical. Whether to load data from SQLite database (default: FALSE).

method

Character. Either "python" or "r" (default: "r").

signed

Logical. Whether to use signed connectivity matrix (default: FALSE).

count_thresh

Numeric. Minimum threshold count (default: 3).

const

Numeric. Adjustment constant for the log transform applied by the R backend's calculate_influence(). See influence_calculator_r() for details.

inhibitory_nts

Character vector of top_nt values whose pre-neurons get negated weights when signed = TRUE. Required for signed mode.

excluded_nts

Character vector of top_nt values whose pre-neurons contribute nothing to W. Independent of signed.

lambda_max

Numeric in (0, 1). Target spectral radius of the rescaled matrix; default 0.99. See influence_calculator_r().

syn_weight_measure

Character, one of "norm" (default) or "count". Edge column used to populate W; forwarded to both backends. See influence_calculator_r() for the trade-off.

Value

InfluenceCalculator object (either Python or R implementation)

Examples

if (FALSE) { # \dontrun{
# Use R implementation with data frames (recommended)
ic.df <- influence_calculator(edgelist_simple = my_edges, meta = my_meta)

# Use R implementation with SQLite database
ic.sqlite <- influence_calculator(filename = "connectome.sqlite", sqlite = TRUE)

# Use Python implementation directly with data frames (no temp SQLite needed)
ic.py <- influence_calculator(edgelist_simple = my_edges, meta = my_meta,
                               method = "python")
} # }