generate_transition_system: Generate a transition system

In simulab: Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Generate a transition system

Description

Generate a transition system

Usage

generate_transition_system(
  n_states = 8L,
  states = NULL,
  concentration = 1,
  diagonal_concentration = 0,
  state_categories = c("metacognitive", "cognitive"),
  seed = NULL,
  batch = NULL
)

Arguments

n_states	Number of states.
states	Optional state labels.
concentration	Dirichlet concentration applied to every transition and to the initial-state probabilities.
diagonal_concentration	Optional extra concentration added to the self-transition (diagonal) entries only. The default 0 leaves self-transitions no more likely than any other transition.
state_categories	Learning-state categories the state labels are drawn from with sample_learning_states() when states is NULL, defaulting to c("metacognitive", "cognitive"); see learning_state_categories(). NULL labels the states ⁠State 1⁠, ⁠State 2⁠, and so on.
seed	Optional random seed.
batch	Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A tidy transition-edge simulab_sim base data.frame with n_states^2 rows and columns from, to, and probability; every from state's probabilities sum to one. An initial_probabilities table (state, probability) is available through as.data.frame().

Examples

result <- generate_transition_system(n_states = 3, seed = 1)
result

simulab documentation built on Oct. 15, 2026, 5:08 p.m.