Compute time-varying Gazepoint transition matrices — compute_gazepoint_time_varying_transition

Compute transition-count and transition-probability matrices across time windows. This helper is a convenience wrapper for studies where AOI/state transitions are expected to vary over the course of a stimulus, trial, or analysis window.

Usage

compute_gazepoint_time_varying_transition_matrix(
  data,
  from_col = NULL,
  to_col = NULL,
  time_col = NULL,
  window_col = NULL,
  window_size_ms = NULL,
  by_cols = NULL,
  count_col = NULL,
  states = NULL,
  complete_states = TRUE,
  drop_self_transitions = FALSE,
  normalise = c("row", "global", "none"),
  name = "gazepoint_time_varying_transition_matrix"
)

Arguments

data: A data frame containing transition-level rows.
from_col: Transition origin column. If NULL, common origin columns are detected automatically.
to_col: Transition destination column. If NULL, common destination columns are detected automatically.
time_col: Optional numeric time column used to construct windows when window_col = NULL.
window_col: Optional existing time-window column.
window_size_ms: Numeric window size used when window_col = NULL.
by_cols: Optional grouping columns, such as subject, condition, trial, or stimulus.
count_col: Optional count/weight column. If NULL, each row contributes one transition.
states: Optional character vector of allowed states/AOIs. If NULL, states are detected from from_col and to_col.
complete_states: If TRUE, complete all state-pair combinations within each time window and group.
drop_self_transitions: If TRUE, remove transitions where origin and destination are the same.
normalise: Probability normalisation. Options are "row", "global", and "none".
name: Character label stored in the returned object.

Value

A list with class gp3_time_varying_transition_matrix.