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Compute Count Tables for STM and LTM with Optional Prior

count_tables.Rd

Generates count tables for STM (per-timestep) and/or LTM (per-context), optionally incorporating previously accumulated LTM counts.

Usage

count_tables(
  x,
  N,
  alphabet,
  model_type = c("stm", "ltm", "both"),
  prior = list(),
  stm_update_exclusion = FALSE,
  ltm_update_exclusion = FALSE,
  ltm_start_token = TRUE
)

Arguments

x

Character vector of symbols/events.

N

Maximum N-gram order.

alphabet

Character vector of all possible symbols.

model_type

Character: one of `"stm"`, `"ltm"`, `"both"`.

prior

Optional: previously accumulated LTM tables (list of length N+1). Used to initialize/accumulate counts for LTM or `both` type.

stm_update_exclusion

Logical; apply update exclusion in STM (default TRUE). Prevents lower-order updates when an event is already observed in a higher-order context at the same timestep.

ltm_update_exclusion

Logical; apply update exclusion in LTM (default FALSE). If TRUE, applies the same exclusion logic during corpus accumulation; if FALSE, all orders are updated for every event.

Value

A list with elements depending on `model_type`: - `$stm`: list of length N+1, each a data.table of counts per timestep (if `model_type` includes `"stm"`). - `$ltm`: list of length N+1, each a data.table of counts per context (if `model_type` includes `"ltm"`).