jaatha (version 3.0.0)

jaatha: Simulation based maximum likelihood estimation

Description

Simulation based maximum likelihood estimation

Usage

jaatha(model, data, repetitions = 3, sim = model$get_par_number() * 25,
  max_steps = 100, init_method = c("initial-search", "zoom-in", "middle"),
  cores = 1, verbose = TRUE)

Arguments

model

The model used for the estimation. See create_jaatha_model.

data

The data used for the estimation. See create_jaatha_data.

repetitions

The number of independend optimizations that will be conducted. You should use a value greater than one here, to minimize the chance that the algorithms is stuck in a local maximum.

sim

The number of simulations conducted for each step.

max_steps

The maximal number of steps, in case Jaatha fails to converge.

init_method

Determines how the starting position of each repetition is chosen. See below for a description of the different options.

cores

The number of CPU cores that will be used for the simulations. The relies on the parallel package, and consequenlty only one core is supported on Windows.

verbose

If TRUE, information about the optimization algorithm is printed.

Value

• TBR

Algorithm

TBR

Initialization Methods

TBR