data	The dataset with the indicators.
indicators	The variable names of the indicators.
n_state	The number of states that should be estimated when modelselection = FALSE
n_fact	The number of factors that should be estimated when modelselection = FALSE
modelselection	The indication whether model selection should be performed or not. The default is FALSE.
n_state_range	The range of states that should be estimated when modelselection = TRUE.
n_fact_range	The range of factors that should be estimated when modelselection = TRUE.
n_starts	The number of start sets. Multiple start sets are required in order to increase the chances of finding the global maximum (for details, see Supplementary Material S.3.5). The default is 25.
n_initial_ite	The number of initial iterations, that is, the number of iterations that is performed for each start set (for an explanation, see Supplementary Material S.3.5). The default is 15.
n_m_step	The number of maximization-step iterations inside the implemented expectation maximization algorithm (for details, see Supplementary Material S.3). The default is 10.
em_tolerance	The estimation convergence criterion (for details, see Supplementary Material S.3.4). The default is 1e-8.
m_step_tolerance	The criterion for stopping the maximization-step iterations. The default is 1e-3. Thus, the maximization-step iterations stop when either m_step_tolerance or n_m_step has been reached.
max_iterations	The maximum number of iterations after which the estimation terminates regardless of whether convergence has been reached or not. The default is 1000 iterations.
n_mclust	The number of mclust start sets (for details, see Supplementary Material S.3.5). The default is 5.