build_atna: Build an Attention-Weighted Transition Network (ATNA)

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Build an Attention-Weighted Transition Network (ATNA)

Description

Convenience wrapper for build_network(method = "attention"). Computes decay-weighted transitions from sequence data.

Usage

build_atna(data, start = FALSE, end = FALSE, ...)

Arguments

data	Data frame (sequences or per-observation frequencies) or a square symmetric matrix (correlation or covariance).
start	Boundary marker prepended to every sequence as an explicit start state (a pure source: no incoming edges, every sequence's first transition is start -> first_observed). FALSE (default) adds nothing; TRUE uses the label "Start"; a single string uses that string as the label. Only valid for the transition methods (relative, frequency, co_occurrence, attention); errors otherwise.
end	Boundary marker placed in the single cell after each sequence's last observed (non-NA) state, as an explicit terminal state (a pure sink: no outgoing edges, no self-loop – distinct from mark_terminal_state, which fills all trailing NAs into an absorbing state). FALSE (default) adds nothing; TRUE uses the label "End"; a single string uses that string as the label. Same method restriction as start.
...	Additional arguments passed to build_network.

Value

A netobject (see build_network).

See Also

build_network

Examples

seqs <- data.frame(V1 = c("A","B","C"), V2 = c("B","C","A"))
net <- build_atna(seqs)

Nestimate documentation built on July 11, 2026, 1:09 a.m.