R/simdesign_helper.R

In nlrx: Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'

#' Add a simple simdesign to a nl object
#'
#' @description Add a simple simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#' The simple simdesign only uses model parameters that are defined in the constants field of the experiment object within the nl object.
#' Thus, the resulting input tibble of the simdesign has only one run with constant parameterisations.
#' This can be useful to run one simulation with a specific parameterset.
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_simple
#' nl@@simdesign <- simdesign_simple(nl = nl, nseeds = 3)
#'
#'
#' @aliases simdesign_simple
#' @rdname simdesign_simple
#'
#' @export

simdesign_simple <- function(nl, nseeds) {

  util_eval_experiment(nl)
  util_eval_constants(nl)
  message("Creating simple simulation design")
  # This doesnt use variables but only constants to create a simdesign:
  simple <- tibble::as_tibble(getexp(nl, "constants"))
  seeds <- util_generate_seeds(nseeds = nseeds)

  new_simdesign <- simdesign(simmethod="simple",
                             siminput=simple,
                             simseeds=seeds)

  return(new_simdesign)

}



#' Add a distinct simdesign to a nl object
#'
#' @description Add a distinct simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#' The distinct simdesign allows to create a parameter matrix with distinct parameterisations.
#'
#' Variables in the experiment variable list need to provide a vector of distinct values (e.g. list(values=c(1,2,3,4)).
#' All vectors of values must have the same length across variables.
#'
#' The distinct simdesign then creates one simulation run for all first elements of these values vectors,
#' one run for all second items, and so on.
#' With this function, multiple distinct simulations can be run at once.
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_distinct
#' nl@@simdesign <- simdesign_distinct(nl = nl, nseeds = 3)
#'
#'
#' @aliases simdesign_distinct
#' @rdname simdesign_distinct
#'
#' @export

simdesign_distinct <- function(nl, nseeds) {

  util_eval_experiment(nl)
  util_eval_variables_distinct(nl)
  message("Creating distinct simulation design")

  # Create a tibble from the defined variable value vectors:

  ff <- purrr::map(getexp(nl, "variables"), function(i) {

    ## If values are not directly supplied generate values from distribution:
      i$values

  })

  ff <- tibble::as_tibble(ff)

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    ff <- tibble::as_tibble(cbind(ff, getexp(nl, "constants"), stringsAsFactors=FALSE))
  }

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds = nseeds)

  new_simdesign <- simdesign(simmethod="distinct",
                             siminput=ff,
                             simseeds=seeds)

  return(new_simdesign)

}




#' Add a full-factorial simdesign to a nl object
#'
#' @description Add a full-factorial simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a vector of distinct values (e.g. list(values=c(1,2,3,4)).
#' Or a sequence definition with min, max and step (e.g. list=(min=1, max=4, step=1)).
#' If both (values and sequence) are defined, the full-factorial design gives priority to the values.
#'
#' The full-factorial simdesign uses these defined parameter ranges within the nl object.
#' A full-factorial matrix of all parameter combinations is created as input tibble for the simdesign.
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_ff
#' nl@@simdesign <- simdesign_ff(nl = nl, nseeds = 3)
#'
#'
#' @aliases simdesign_ff
#' @rdname simdesign_ff
#'
#' @export

simdesign_ff <- function(nl, nseeds) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_ff(nl)
  message("Creating full factorial simulation design")


  # Add a full factorial simulatin design:
  # Generate vectors from variables data:
  ff <- purrr::map(getexp(nl, "variables"), function(i) {

    ## If values are not directly supplied generate values from distribution:
    if (is.null(i$values)) {
      # generate a sequence:
      seq(i$min, i$max, i$step)
    } else {
      i$values
    }
  })

  ff <- tibble::as_tibble(expand.grid(ff, stringsAsFactors = FALSE))

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    ff <- tibble::as_tibble(cbind(ff, getexp(nl, "constants"), stringsAsFactors=FALSE))
  }

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds = nseeds)

  new_simdesign <- simdesign(simmethod="ff",
                             siminput=ff,
                             simseeds=seeds)

  return(new_simdesign)
}

#' Add a latin-hypercube simdesign to a nl object
#'
#' @description Add a latin-hypercube simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param samples number of samples for the latin hypercube
#' @param nseeds number of seeds for this simulation design
#' @param precision number of digits for the decimal fraction of parameter values
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and qfun (e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The latin hypercube simdesign creates a parameter matrix based on these defined distributions.
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_lhs
#' nl@@simdesign <- simdesign_lhs(nl=nl,
#'                                samples=100,
#'                                nseeds=3,
#'                                precision=3)
#'
#' @aliases simdesign_lhs
#' @rdname simdesign_lhs
#'
#' @export

simdesign_lhs <- function(nl, samples, nseeds, precision) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_sa(nl)
  message("Creating latin hypercube simulation design")

  lhs <- util_create_lhs(input = getexp(nl, "variables"),
                         samples = samples,
                         precision = precision)

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    lhs <- cbind(lhs, getexp(nl, "constants"), stringsAsFactors=FALSE)
  }

  ## Convert to tibble:
  lhs <- tibble::as_tibble(lhs)

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds = nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="lhs",
                             siminput=lhs,
                             simseeds=seeds)

  return(new_simdesign)
}

#' Add a sobol simdesign to a nl object
#'
#' @description Add a sobol simdesign to a nl object
#' @return simdesign S4 class object
#' @param nl nl object with a defined experiment
#' @param samples number of samples for the sobol sensitivity analysis
#' @param sobolorder order of interactions of the sobol sensitivity analysis
#' @param sobolnboot number of bootstrap replicates of the sobol sensitivity analysis
#' @param sobolconf the confidence level for bootstrap confidence intervals
#' @param nseeds number of seeds for this simulation design
#' @param precision number of digits for the decimal fraction of parameter values
#'
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and qfun (e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The sobol simdesign uses the sensitivity package to set up a sobol sensitivity analysis, including a simobject of class sobol and a input tibble for simulations.
#' For details on method specific sensitivity analysis function parameters see ?sobol
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_sobol
#' nl@@simdesign <- simdesign_sobol(nl=nl,
#' samples=1000,
#' sobolorder=2,
#' sobolnboot=100,
#' sobolconf=0.95,
#' nseeds=3,
#' precision=3)
#'
#'
#' @aliases simdesign_sobol
#' @rdname simdesign_sobol
#'
#' @export

simdesign_sobol <- function(nl,
                            samples,
                            sobolorder,
                            sobolnboot,
                            sobolconf,
                            nseeds,
                            precision) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_sa(nl)

  ## Additionally check that sobolorder is <= number of variables
  if(sobolorder > length(getexp(nl, "variables"))) {
    stop("Sobolorder must be lower or equal than the number of defined variables in your experiment!")
  }

  message("Creating sobol simulation design")

  lhs_1 <- util_create_lhs(input = getexp(nl, "variables"),
                           samples = samples,
                           precision = precision)
  lhs_2 <- util_create_lhs(input = getexp(nl, "variables"),
                           samples = samples,
                           precision = precision)

  # create instance of sobol class
  so <- sensitivity::sobol(model = NULL,
                           X1 = lhs_1,
                           X2 = lhs_2,
                           order=sobolorder,
                           nboot = sobolnboot,
                           conf=sobolconf)

  ## Export parameter matrix:
  soX <- so$X

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    soX <- cbind(soX, getexp(nl, "constants"), stringsAsFactors=FALSE)
  }
  ## Convert to tibble
  soX <- tibble::as_tibble(soX)

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds=nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="sobol",
                             siminput=soX,
                             simobject=list(so),
                             simseeds=seeds)

  ## Check if the simdesign contains NAs
  .util_check_siminput_tibble(nl, new_simdesign)

  return(new_simdesign)
}

#' Add a sobol2007 simdesign to a nl object
#'
#' @description Add a sobol2007 simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param samples number of samples for the sobol sensitivity analysis
#' @param sobolnboot number of bootstrap replicates of the sobol sensitivity analysis
#' @param sobolconf the confidence level for bootstrap confidence intervals
#' @param nseeds number of seeds for this simulation design
#' @param precision number of digits for the decimal fraction of parameter values
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and qfun (e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The sobol2007 simdesign uses the sensitivity package to set up a sobol2007 sensitivity analysis, including a simobject of class sobol and a input tibble for simulations.
#' For details on method specific sensitivity analysis function parameters see ?sobol2007
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_sobol2007
#' nl@@simdesign <- simdesign_sobol2007(nl=nl,
#' samples=1000,
#' sobolnboot=100,
#' sobolconf=0.95,
#' nseeds=3,
#' precision=3)
#'
#'
#' @aliases simdesign_sobol2007
#' @rdname simdesign_sobol2007
#'
#' @export

simdesign_sobol2007 <- function(nl,
                                samples,
                                sobolnboot,
                                sobolconf,
                                nseeds,
                                precision) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_sa(nl)
  message("Creating sobol2007 simulation design")

  lhs_1 <- util_create_lhs(input = getexp(nl, "variables"),
                           samples = samples,
                           precision = precision)
  lhs_2 <- util_create_lhs(input = getexp(nl, "variables"),
                           samples = samples,
                           precision = precision)

  # create instance of sobol class
  so <- sensitivity::sobol2007(model = NULL,
                               X1 = lhs_1,
                               X2 = lhs_2,
                               nboot = sobolnboot,
                               conf=sobolconf)

  # Export parameter matrix:
  soX <- so$X

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    soX <- cbind(soX, getexp(nl, "constants"), stringsAsFactors=FALSE)
  }

  ## Convert to tibble
  soX <- tibble::as_tibble(soX)

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds=nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="sobol2007",
                             siminput=soX,
                             simobject=list(so),
                             simseeds=seeds)

  ## Check if the simdesign contains NAs
  .util_check_siminput_tibble(nl, new_simdesign)

  return(new_simdesign)
}


#' Add a soboljansen simdesign to a nl object
#'
#' @description Add a soboljansen simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param samples number of samples for the sobol sensitivity analysis
#' @param sobolnboot number of bootstrap replicates of the sobol sensitivity analysis
#' @param sobolconf the confidence level for bootstrap confidence intervals
#' @param nseeds number of seeds for this simulation design
#' @param precision number of digits for the decimal fraction of parameter values
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and qfun (e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The soboljansen simdesign uses the sensitivity package to set up a soboljansen sensitivity analysis, including a simobject of class sobol and a input tibble for simulations.
#' For details on method specific sensitivity analysis function parameters see ?soboljansen
#' Finally, the function reports a simdesign object.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_soboljansen
#' nl@@simdesign <- simdesign_soboljansen(nl=nl,
#' samples=1000,
#' sobolnboot=100,
#' sobolconf=0.95,
#' nseeds=3,
#' precision=3)
#'
#'
#' @aliases simdesign_soboljansen
#' @rdname simdesign_soboljansen
#'
#' @export

simdesign_soboljansen <- function(nl,
                                  samples,
                                  sobolnboot,
                                  sobolconf,
                                  nseeds,
                                  precision) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_sa(nl)
  message("Creating soboljansen simulation design")

  lhs_1 <- util_create_lhs(input = getexp(nl, "variables"),
                           samples = samples,
                           precision = precision)
  lhs_2 <- util_create_lhs(input = getexp(nl, "variables"),
                           samples = samples,
                           precision = precision)

  # create instance of sobol class
  so <- sensitivity::soboljansen(model = NULL,
                                 X1 = lhs_1,
                                 X2 = lhs_2,
                                 nboot = sobolnboot,
                                 conf=sobolconf)

  # Export parameter matrix:
  soX <- so$X

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    soX <- cbind(soX, getexp(nl, "constants"), stringsAsFactors=FALSE)
  }

  ## Convert to tibble
  soX <- tibble::as_tibble(soX)

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds=nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="soboljansen",
                             siminput=soX,
                             simobject=list(so),
                             simseeds=seeds)

  ## Check if the simdesign contains NAs
  .util_check_siminput_tibble(nl, new_simdesign)

  return(new_simdesign)
}


#' Add a morris elementary effects simdesign to a nl object
#'
#' @description Add a morris elementary effects simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param morristype morris design type
#' @param morrislevels number of parameter levels
#' @param morrisr morris r value
#' @param morrisgridjump morris grid jump value
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and qfun (e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The morris simdesign uses the sensitivity package to set up a morris elementary effects sensitivity analysis, including a simobject of class morris and a input tibble for simulations.
#' For details on method specific sensitivity analysis function parameters see ?morris
#' Finally, the function reports a simdesign object.
#'
#'
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_morris
#' nl@@simdesign <- simdesign_morris(nl=nl,
#'                                   morristype="oat",
#'                                   morrislevels=4,
#'                                   morrisr=20,
#'                                   morrisgridjump=2,
#'                                   nseeds=3)
#'
#'
#' @aliases simdesign_morris
#' @rdname simdesign_morris
#'
#' @export

simdesign_morris <- function(nl,
                             morristype,
                             morrislevels,
                             morrisr,
                             morrisgridjump,
                             nseeds) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_sa(nl)

  if (morrisgridjump > morrislevels)
  {
    warning("morrisgridjump should be lower than morrislevels. Morris recommendation is to use (morrislevels / 2) for the gridjump value. The parameter matrix might be invalid. Please adjust and attach the simdesign again.")
  }
  message("Creating morris simulation design")

  morrisdesign <- list(type = morristype,
                       levels = morrislevels,
                       grid.jump = morrisgridjump)

  # get the min and max values of the input factor ranges
  mins <- unlist(lapply(getexp(nl, "variables"), "[", "min"))
  maxs <- unlist(lapply(getexp(nl, "variables"), "[", "max"))

  # create input sets
  mo <- sensitivity::morris(model = NULL,
                            factors = names(getexp(nl, "variables")),
                            r = morrisr,
                            design = morrisdesign,
                            binf = mins,
                            bsup = maxs,
                            scale=TRUE)

  # Export parameter matrix:
  moX <- tibble::as_tibble(mo$X)

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    moX <- cbind(moX, getexp(nl, "constants"), stringsAsFactors=FALSE)
  }

  ## Convert to tibble
  moX <- tibble::as_tibble(moX)

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="morris",
                             siminput=moX,
                             simobject=list(mo),
                             simseeds=seeds)

  ## Check if the simdesign contains NAs
  .util_check_siminput_tibble(nl, new_simdesign)

  return(new_simdesign)

}


#' Add an eFast simdesign to a nl object
#'
#' @description Add an eFast simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param samples number of samples for the eFast sensitivity analysis
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and qfun (e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The eFast simdesign uses the sensitivity package to set up a fast99 elementary effects sensitivity analysis, including a simobject of class fast99 and a input tibble for simulations.
#' For details on method specific sensitivity analysis function parameters see ?fast99
#' Finally, the function reports a simdesign object.
#'
#'
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_eFast
#' nl@@simdesign <- simdesign_eFast(nl=nl,
#'                                  samples=100,
#'                                  nseeds=1)
#'
#'
#' @aliases simdesign_eFast
#' @rdname simdesign_eFast
#'
#' @export

simdesign_eFast <- function(nl,
                            samples,
                            nseeds) {

  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_sa(nl)
  message("Creating eFast simulation design")

  # get names of quantile functions fpr the input factors
  q.functions <- unlist(lapply(getexp(nl, "variables"), "[", "qfun"))

  # generate a list of arguments for the quantile functions
  q.args <- lapply(getexp(nl, "variables"), function(i) {
    i$qfun <- NULL
    i$values <- NULL
    i$step <- NULL; return(i)})

  # create instance of fast99 class
  f99 <- sensitivity::fast99(model = NULL,
                             factors = names(getexp(nl, "variables")),
                             n = samples,
                             q = q.functions,
                             q.arg = q.args)

  # Export parameter matrix:
  f99X <- tibble::as_tibble(f99$X)

  ## Bind constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    f99X <- cbind(f99X, getexp(nl, "constants"), stringsAsFactors=FALSE)
  }

  ## Convert to tibble
  f99X <- tibble::as_tibble(f99X)

  ## Generate seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="eFast",
                             siminput=f99X,
                             simobject=list(f99),
                             simseeds=seeds)

  ## Check if the simdesign contains NAs
  .util_check_siminput_tibble(nl, new_simdesign)

  return(new_simdesign)

}




#' Add a Simulated Annealing simdesign to a nl object
#'
#' @description Add a Simulated Annealing simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param par optional vector of start values for each parameter defined in variables of experiment
#' @param evalcrit position of evaluation criterion within defined NetLogo metrics of nl experiment or a function that reports a single numeric value
#' @param control list with further arguments passed to the GenSA function (see ?GenSA for details)
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min and max (e.g. list(min=1, max=4)).
#'
#' The GenSA simdesign generates a simulated Annealing experiment within the defined min and max parameter boundaries
#' that are defined in the variables field of the experiment object within the nl object.
#'
#' The evalcrit reporter defines the evaluation criterion for the simulated annealing procedure.
#' There are two options to evaluate the fitness value of each iteration of the algorithm:
#' 1. Use a reporter that is defined within the experiment metrics vector.
#' You can just enter the position of that metric within the experiment metrics vector (e.g. 1 would use the first defined metric of the experiment to evaluate each iteration).
#' The algorithm automatically calculates the mean value of this reporter if evalticks is defined to measure multiple ticks during each simulation.
#' You can define a function that post-processes NetLogo output to calculate an evaluation value. This function must accept the nl object as input and return one single numeric value.
#' The nl object that is then provided to the evaluation function will have results of the current iteration attached. The results can be accessed via the simoutput slot of the simdesign.
#' You can pass this function to evalcrit. It is then applied to the output of each iteration.
#'
#' The function uses the GenSA package to set up a Simulated Annealing function.
#' For details on the GenSA function parameters see ?GenSA
#' Finally, the function reports a simdesign object.
#'
#' Simulated Annealing simdesigns can only be executed using the \link[nlrx]{run_nl_dyn} function instead of \link[nlrx]{run_nl_all} or \link[nlrx]{run_nl_one}.
#'
#'
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_lhs
#'
#' # Example 1: Using a metric from the experiment metrics vector for evaluation:
#' nl@@simdesign <- simdesign_GenSA(nl=nl,
#'                                  par=NULL,
#'                                  evalcrit=1,
#'                                  control=list(max.time = 600),
#'                                  nseeds=1)
#'
#'
#' # Example 2: Using a self-defined evaluation function
#' # For demonstration we define a simple function that calculates
#' # the maximum value of count sheep output.
#' critfun <- function(nl) {
#' results <- nl@@simdesign@@simoutput
#' crit <- as.integer(max(results$`count sheep`))
#' return(crit)
#' }
#'
#' nl@@simdesign <- simdesign_GenSA(nl=nl,
#'                                  par=NULL,
#'                                  evalcrit=critfun,
#'                                  control=list(max.time = 600),
#'                                  nseeds=1)
#'
#'
#' @aliases simdesign_GenSA
#' @rdname simdesign_GenSA
#'
#' @export

simdesign_GenSA <- function(nl,
                            par = NULL,
                            evalcrit = 1,
                            control = list(),
                            nseeds = 1) {

  # Evaluate experiment and variables:
  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_op(nl)
  message("Creating GenSA simulation design")

  # Parameters we need for simulated annealing:
  lower <- unlist(lapply(getexp(nl, "variables"), "[", "min"))
  upper <- unlist(lapply(getexp(nl, "variables"), "[", "max"))

  # Get evaulation criterion reporter from metrics vector or use supplied function:
  if(is.function(evalcrit)){
    evalcrit_reporter <- evalcrit
  } else {
    evalcrit_reporter <- getexp(nl, "metrics")[evalcrit]
  }

  # Check if the reporter exists:
  if (suppressWarnings(is.na(evalcrit_reporter))) {
    stop(paste0("Error: No valid reporter at defined evalcrit position: ",
                evalcrit))
  }

  # Create a gsa object:
  gsa <- list(par = par,
              upper = upper,
              lower = lower,
              evalcrit = evalcrit_reporter,
              control = control)

  # generate random seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="GenSA",
                             siminput=tibble::tibble(),
                             simobject=gsa,
                             simseeds=seeds)

  return(new_simdesign)

}





#' Add a Genetic Algorithm simdesign to a nl object
#'
#' @description Add a Genetic Algorithm simdesign to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param popSize population Size parameter for genetic algorithm
#' @param iters number of iterations for genetic algorithm function
#' @param evalcrit position of evaluation criterion within defined NetLogo metrics of nl experiment or a function that reports a single numeric value
#' @param elitism elitism rate of genetic algorithm function
#' @param mutationChance mutation rate of genetic algorithm function
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min and max (e.g. list(min=1, max=4)).
#'
#' The GenAlg simdesign generates a Genetic Algorithm experiment within the defined min and max parameter boundaries
#' that are defined in the variables field of the experiment object within the nl object.
#'
#' The evalcrit reporter defines the evaluation criterion for the Genetic algorithm procedure.
#' There are two options to evaluate the fitness value of each iteration of the algorithm:
#' 1. Use a reporter that is defined within the experiment metrics vector.
#' You can just enter the position of that metric within the experiment metrics vector (e.g. 1 would use the first defined metric of the experiment to evaluate each iteration).
#' The algorithm automatically calculates the mean value of this reporter if evalticks is defined to measure multiple ticks during each simulation.
#' 2. Use a self-defined evaluation function
#' You can define a function that post-processes NetLogo output to calculate an evaluation value. This function must accept the nl object as input and return one single numeric value.
#' The nl object that is then provided to the evaluation function will have results of the current iteration attached. The results can be accessed via the simoutput slot of the simdesign.
#' You can pass this function to evalcrit. It is then applied to the output of each iteration.
#'
#' The function uses the genalg package to set up a Genetic Algorithm function.
#' For details on the genalg function parameters see ?genalg::rbga
#' Finally, the function reports a simdesign object.
#'
#' Genetic Algorithm simdesigns can only be executed using the \link[nlrx]{run_nl_dyn} function instead of \link[nlrx]{run_nl_all} or \link[nlrx]{run_nl_one}.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_lhs
#'
#' # Example 1: Using a metric from the experiment metrics vector for evaluation:
#' nl@@simdesign <- simdesign_GenAlg(nl=nl,
#'                                   evalcrit=1,
#'                                   nseeds=1)
#'
#' # Example 2: Using a self-defined evaluation function
#' # For demonstration we define a simple function that calculates
#' # the maximum value of count sheep output.
#' critfun <- function(nl) {
#' results <- nl@@simdesign@@simoutput
#' crit <- as.integer(max(results$`count sheep`))
#' return(crit)
#' }
#'
#' nl@@simdesign <- simdesign_GenAlg(nl=nl,
#'                                   evalcrit=critfun,
#'                                   nseeds=1)
#'
#'
#' @aliases simdesign_GenAlg
#' @rdname simdesign_GenAlg
#'
#' @export

simdesign_GenAlg <- function(nl,
                             popSize = 200,
                             iters = 100,
                             evalcrit = 1,
                             elitism = NA,
                             mutationChance = NA,
                             nseeds = 1) {

  # Evaluate experiment and variables:
  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_op(nl)
  message("Creating GenAlg simulation design")

  # Parameters we need for simulated annealing:
  lower <- unlist(lapply(getexp(nl, "variables"), "[", "min"))
  upper <- unlist(lapply(getexp(nl, "variables"), "[", "max"))

  # Get evaulation criterion reporter from metrics vector or use supplied function:
  if(is.function(evalcrit)){
    evalcrit_reporter <- evalcrit
  } else {
    evalcrit_reporter <- getexp(nl, "metrics")[evalcrit]
  }

  # Check if the reporter exists:
  if (suppressWarnings(is.na(evalcrit_reporter))) {
    stop(paste0("Error: No valid reporter at defined evalcrit position: ",
                evalcrit))
  }

  # Create a gsa object:
  galg <- list(popSize = popSize,
              iters = iters,
              upper = upper,
              lower = lower,
              elitism = elitism,
              mutationChance = mutationChance,
              evalcrit = evalcrit_reporter)

  # generate random seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="GenAlg",
                             siminput=tibble::tibble(),
                             simobject=galg,
                             simseeds=seeds)

  return(new_simdesign)

}


#' Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Majoram algorithm to a nl object
#'
#' @description Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Majoram algorithm to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param postpro_function default is NULL. Allows to provide a function that is called to post-process the output Tibble of the NetLogo simulations. The function must accept the nl object with attached results as input argument. The function must return a one-dimensional vector of output metrics that corresponds in length and order to the specified summary_stat_target.
#' @param summary_stat_target a vector of target values in the same order as the defined metrics of the experiment
#' @param prior_test a string expressing the constraints between model parameters. This expression will be evaluated as a logical expression, you can use all the logical operators including "<", ">", ... Each parameter should be designated with "X1", "X2", ... in the same order as in the prior definition. Set to NULL to disable.
#' @param n_rec Number of samples along the MCMC
#' @param n_between_sampling a positive integer equal to the desired spacing between sampled points along the MCMC.
#' @param n_cluster number of cores to parallelize simulations. Due to the design of the EasyABC parallelization it is currently not possible to use this feature with cores > 1.
#' @param use_seed if TRUE, seeds will be automatically created for each new model run
#' @param dist_weights a vector containing the weights to apply to the distance between the computed and the targeted statistics. These weights can be used to give more importance to a summary statistic for example. The weights will be normalized before applying them. Set to NULL to disable.
#' @param n_calibration a positive integer. This is the number of simulations performed during the calibration step. Default value is 10000.
#' @param tolerance_quantile a positive number between 0 and 1 (strictly). This is the percentage of simulations retained during the calibration step to determine the tolerance threshold to be used during the MCMC. Default value is 0.01.
#' @param proposal_phi a positive number. This is a scaling factor defining the range of MCMC jumps. Default value is 1.
#' @param seed_count a positive integer, the initial seed value provided to the function model (if use_seed=TRUE). This value is incremented by 1 at each call of the function model.
#' @param progress_bar logical, FALSE by default. If TRUE, ABC_mcmc will output a bar of progression with the estimated remaining computing time. Option not available with multiple cores.
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and a shape of the distribution (qunif, qnorm, qlnorm, qexp)(e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The function uses the EasyABC package to set up the ABC_mcmc function.
#' For details on the ABC_mcmc function parameters see ?EasyABC::ABC_mcmc
#' Finally, the function reports a simdesign object.
#'
#' Approximate Bayesian Computation simdesigns can only be executed using the \link[nlrx]{run_nl_dyn} function instead of \link[nlrx]{run_nl_all} or \link[nlrx]{run_nl_one}.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_lhs
#'
#' # Attach the simdesign to the nl object
#' nl@@simdesign <- simdesign_ABCmcmc_Marjoram(nl = nl,
#'                                             summary_stat_target = c(100, 80),
#'                                             n_rec = 100,
#'                                             n_between_sampling = 10,
#'                                             n_cluster = 1,
#'                                             use_seed = FALSE,
#'                                             n_calibration = 10000,
#'                                             tolerance_quantile = 0.01,
#'                                             proposal_phi = 1,
#'                                             progress_bar = FALSE,
#'                                             nseeds = 1)
#'
#' @aliases simdesign_ABCmcmc_Marjoram
#' @rdname simdesign_ABCmcmc_Marjoram
#'
#' @export
simdesign_ABCmcmc_Marjoram <- function(nl,
                                       postpro_function = NULL,
                                       summary_stat_target,
                                       prior_test = NULL,
                                       n_rec,
                                       n_between_sampling = 10,
                                       n_cluster = 1,
                                       use_seed = FALSE,
                                       dist_weights = NULL,
                                       n_calibration = 10000,
                                       tolerance_quantile = 0.01,
                                       proposal_phi = 1,
                                       seed_count = 0,
                                       progress_bar = FALSE,
                                       nseeds)
{
  # Evaluate experiment and variables:
  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_op(nl)
  message("Creating ABC Monte-Carlo Markov-Chain simulation design")

  ## Generating prior data from variables definitions
  prior <- purrr::map(nl@experiment@variables, function(x) {
    qfun <- x$qfun
    if(!qfun %in% c("qunif", "qnorm", "qlnorm", "qexp"))
    {
      stop("Defined qfun not supported. Qfun must be one-of qunif, qnorm, qlnorm, qexp")
    }
    qfun <- ifelse(qfun == "qunif", "unif",
                   ifelse(qfun == "qnorm", "normal",
                          ifelse(qfun == "qlnorm", "lognormal", "exponential")))
    prior.x <- c(qfun, x$min, x$max)
    return(prior.x)
  })

  # Check n_cluster:
  if(n_cluster > 1)
  {
    warning("n_cluster is set to a value > 1. Due to the design of the parallelization of the EasyABC package it is currently not possible to use this feature. n_cluster will be reset to 1.")
    n_cluster <- 1
  }

  # Create an abcmcmc object:
  abcmcmc <- list(method="Marjoram",
                  postpro_function=postpro_function,
                  prior=prior,
                  summary_stat_target=summary_stat_target,
                  prior_test=prior_test,
                  n_rec=n_rec,
                  n_between_sampling=n_between_sampling,
                  n_cluster=n_cluster,
                  use_seed=use_seed,
                  dist_weights=dist_weights,
                  n_calibration=n_calibration,
                  tolerance_quantile=tolerance_quantile,
                  proposal_phi=proposal_phi,
                  seed_count=seed_count,
                  progress_bar=progress_bar)

  # generate random seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="ABCmcmc",
                             siminput=tibble::tibble(),
                             simobject=abcmcmc,
                             simseeds=seeds)

  return(new_simdesign)

}


#' Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Majoram Original algorithm to a nl object
#'
#' @description Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Majoram Original algorithm to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param postpro_function default is NULL. Allows to provide a function that is called to post-process the output Tibble of the NetLogo simulations. The function must accept the nl object with attached results as input argument. The function must return a one-dimensional vector of output metrics that corresponds in leght and order to the specified summary_stat_target.
#' @param summary_stat_target a vector of target values in the same order as the defined metrics of the experiment
#' @param prior_test a string expressing the constraints between model parameters. This expression will be evaluated as a logical expression, you can use all the logical operators including "<", ">", ... Each parameter should be designated with "X1", "X2", ... in the same order as in the prior definition. Set to NULL to disable.
#' @param n_rec Number of samples along the MCMC
#' @param n_between_sampling a positive integer equal to the desired spacing between sampled points along the MCMC.
#' @param n_cluster number of cores to parallelize simulations. Due to the design of the EasyABC parallelization it is currently not possible to use this feature with cores > 1.
#' @param use_seed if TRUE, seeds will be automatically created for each new model run
#' @param dist_weights a vector containing the weights to apply to the distance between the computed and the targeted statistics. These weights can be used to give more importance to a summary statistic for example. The weights will be normalized before applying them. Set to NULL to disable.
#' @param dist_max a positive number. This is the tolerance threshold used during the MCMC. If not provided by the user, it is automatically computed as half the distance between the first simulation and the target summary statistics and a warning is printed.
#' @param tab_normalization a vector of the same length as summary_stat_target. Each element contains a positive number by which each summary statistics must be divided before the computation of the Euclidean distance between simulations and data. If not provided by the user, the simulated summary statistics are divided by the target summary statistics and a warning is printed.
#' @param proposal_range a vector of the same length as the number of model parameters, used when method is "Marjoram_original". Each element contains a positive number defining the range of MCMC jumps for each model parameter. If not provided by the user, a default value is used for each parameter and a warning is printed. The default value is 1/50 of the prior range for uniform distributions, 1/20 of the standard deviation of the prior distribution for normal distributions, 1/20 * exp ( sigma * sigma
#' @param seed_count a positive integer, the initial seed value provided to the function model (if use_seed=TRUE). This value is incremented by 1 at each call of the function model.
#' @param progress_bar logical, FALSE by default. If TRUE, ABC_mcmc will output a bar of progression with the estimated remaining computing time. Option not available with multiple cores.
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and a shape of the distribution (qunif, qnorm, qlnorm, qexp)(e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The function uses the EasyABC package to set up the ABC_mcmc function.
#' For details on the ABC_mcmc function parameters see ?EasyABC::ABC_mcmc
#' Finally, the function reports a simdesign object.
#'
#' Approximate Bayesian Computation simdesigns can only be executed using the \link[nlrx]{run_nl_dyn} function instead of \link[nlrx]{run_nl_all} or \link[nlrx]{run_nl_one}.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_lhs
#'
#' # Attach the simdesign to the nl object
#' nl@@simdesign <- simdesign_ABCmcmc_Marjoram_original(nl = nl,
#'                                                      summary_stat_target = c(100, 80),
#'                                                      n_rec = 100,
#'                                                      n_between_sampling = 10,
#'                                                      nseeds = 1)
#'
#' @aliases simdesign_ABCmcmc_Marjoram_original
#' @rdname simdesign_ABCmcmc_Marjoram_original
#'
#' @export
simdesign_ABCmcmc_Marjoram_original <- function(nl,
                                                postpro_function = NULL,
                                                summary_stat_target,
                                                prior_test = NULL,
                                                n_rec,
                                                n_between_sampling = 10,
                                                n_cluster = 1,
                                                use_seed = FALSE,
                                                dist_weights = NULL,
                                                dist_max = 0,
                                                tab_normalization = summary_stat_target,
                                                proposal_range = vector(mode = "numeric", length = length(getexp(nl, "variables"))),
                                                seed_count = 0,
                                                progress_bar = FALSE,
                                                nseeds)
{
  # Evaluate experiment and variables:
  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_op(nl)
  message("Creating ABC Monte-Carlo Markov-Chain simulation design")

  ## Generating prior data from variables definitions
  prior <- purrr::map(nl@experiment@variables, function(x) {
    qfun <- x$qfun
    if(!qfun %in% c("qunif", "qnorm", "qlnorm", "qexp"))
    {
      stop("Defined qfun not supported. Qfun must be one-of qunif, qnorm, qlnorm, qexp")
    }
    qfun <- ifelse(qfun == "qunif", "unif",
                   ifelse(qfun == "qnorm", "normal",
                          ifelse(qfun == "qlnorm", "lognormal", "exponential")))
    prior.x <- c(qfun, x$min, x$max)
    return(prior.x)
  })

  # Check n_cluster:
  if(n_cluster > 1)
  {
    warning("n_cluster is set to a value > 1. Due to the design of the parallelization of the EasyABC package it is currently not possible to use this feature. n_cluster will be reset to 1.")
    n_cluster <- 1
  }

  # Create an abcmcmc object:
  abcmcmc <- list(method="Marjoram_original",
                  postpro_function=postpro_function,
                  prior=prior,
                  summary_stat_target=summary_stat_target,
                  prior_test=prior_test,
                  n_rec=n_rec,
                  n_between_sampling=n_between_sampling,
                  n_cluster=n_cluster,
                  use_seed=use_seed,
                  dist_weights=dist_weights,
                  dist_max=dist_max,
                  tab_normalization=tab_normalization,
                  proposal_range=proposal_range,
                  seed_count=seed_count,
                  progress_bar=progress_bar)

  # generate random seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="ABCmcmc",
                             siminput=tibble::tibble(),
                             simobject=abcmcmc,
                             simseeds=seeds)

  return(new_simdesign)

}


#' Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Wegmann algorithm to a nl object
#'
#' @description Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Wegmann algorithm to a nl object
#'
#' @param nl nl object with a defined experiment
#' @param postpro_function default is NULL. Allows to provide a function that is called to post-process the output Tibble of the NetLogo simulations. The function must accept the nl object with attached results as input argument. The function must return a one-dimensional vector of output metrics that corresponds in leght and order to the specified summary_stat_target.
#' @param summary_stat_target a vector of target values in the same order as the defined metrics of the experiment
#' @param prior_test a string expressing the constraints between model parameters. This expression will be evaluated as a logical expression, you can use all the logical operators including "<", ">", ... Each parameter should be designated with "X1", "X2", ... in the same order as in the prior definition. Set to NULL to disable.
#' @param n_rec Number of samples along the MCMC
#' @param n_between_sampling a positive integer equal to the desired spacing between sampled points along the MCMC.
#' @param n_cluster number of cores to parallelize simulations. Due to the design of the EasyABC parallelization it is currently not possible to use this feature with cores > 1.
#' @param use_seed if TRUE, seeds will be automatically created for each new model run
#' @param dist_weights a vector containing the weights to apply to the distance between the computed and the targeted statistics. These weights can be used to give more importance to a summary statistic for example. The weights will be normalized before applying them. Set to NULL to disable.
#' @param n_calibration a positive integer. This is the number of simulations performed during the calibration step. Default value is 10000.
#' @param tolerance_quantile a positive number between 0 and 1 (strictly). This is the percentage of simulations retained during the calibration step to determine the tolerance threshold to be used during the MCMC. Default value is 0.01.
#' @param proposal_phi a positive number. This is a scaling factor defining the range of MCMC jumps. Default value is 1.
#' @param numcomp a positive integer. This is the number of components to be used for PLS transformations. Default value is 0 which encodes that this number is equal to the number of summary statistics.
#' @param seed_count a positive integer, the initial seed value provided to the function model (if use_seed=TRUE). This value is incremented by 1 at each call of the function model.
#' @param progress_bar logical, FALSE by default. If TRUE, ABC_mcmc will output a bar of progression with the estimated remaining computing time. Option not available with multiple cores.
#' @param nseeds number of seeds for this simulation design
#' @return simdesign S4 class object
#' @details
#'
#' This function creates a simdesign S4 class which can be added to a nl object.
#'
#' Variables in the experiment variable list need to provide a numeric distribution with min, max and a shape of the distribution (qunif, qnorm, qlnorm, qexp)(e.g. list(min=1, max=4, qfun="qunif")).
#'
#' The function uses the EasyABC package to set up the ABC_mcmc function.
#' For details on the ABC_mcmc function parameters see ?EasyABC::ABC_mcmc
#' Finally, the function reports a simdesign object.
#'
#' Approximate Bayesian Computation simdesigns can only be executed using the \link[nlrx]{run_nl_dyn} function instead of \link[nlrx]{run_nl_all} or \link[nlrx]{run_nl_one}.
#'
#'
#' @examples
#'
#' # To attach a simdesign, a nl object needs to be created first (see ?nl).
#' # For this example, we load a nl object from test data.
#'
#' nl <- nl_lhs
#'
#' # Attach the simdesign to the nl object
#' nl@@simdesign <- simdesign_ABCmcmc_Wegmann(nl = nl,
#'                                             summary_stat_target = c(100, 80),
#'                                             n_rec = 100,
#'                                             n_between_sampling = 10,
#'                                             n_cluster = 1,
#'                                             use_seed = FALSE,
#'                                             n_calibration = 10000,
#'                                             tolerance_quantile = 0.01,
#'                                             proposal_phi = 1,
#'                                             progress_bar = FALSE,
#'                                             nseeds = 1)
#'
#' @aliases simdesign_ABCmcmc_Wegmann
#' @rdname simdesign_ABCmcmc_Wegmann
#'
#' @export
simdesign_ABCmcmc_Wegmann <- function(nl,
                                      postpro_function = NULL,
                                      summary_stat_target,
                                      prior_test = NULL,
                                      n_rec,
                                      n_between_sampling = 10,
                                      n_cluster = 1,
                                      use_seed = FALSE,
                                      dist_weights = NULL,
                                      n_calibration = 10000,
                                      tolerance_quantile = 0.01,
                                      proposal_phi = 1,
                                      numcomp = 0,
                                      seed_count = 0,
                                      progress_bar = FALSE,
                                      nseeds)
{
  # Evaluate experiment and variables:
  util_eval_experiment(nl)
  util_eval_variables(nl)
  util_eval_variables_op(nl)
  message("Creating ABC Monte-Carlo Markov-Chain simulation design")

  ## Generating prior data from variables definitions
  prior <- purrr::map(nl@experiment@variables, function(x) {
    qfun <- x$qfun
    if(!qfun %in% c("qunif", "qnorm", "qlnorm", "qexp"))
    {
      stop("Defined qfun not supported. Qfun must be one-of qunif, qnorm, qlnorm, qexp")
    }
    qfun <- ifelse(qfun == "qunif", "unif",
                   ifelse(qfun == "qnorm", "normal",
                          ifelse(qfun == "qlnorm", "lognormal", "exponential")))
    prior.x <- c(qfun, x$min, x$max)
    return(prior.x)
  })

  # Check n_cluster:
  if(n_cluster > 1)
  {
    warning("n_cluster is set to a value > 1. Due to the design of the parallelization of the EasyABC package it is currently not possible to use this feature. n_cluster will be reset to 1.")
    n_cluster <- 1
  }

  # Create an abcmcmc object:
  abcmcmc <- list(method="Wegmann",
                  postpro_function=postpro_function,
                  prior=prior,
                  summary_stat_target=summary_stat_target,
                  prior_test=prior_test,
                  n_rec=n_rec,
                  n_between_sampling=n_between_sampling,
                  n_cluster=n_cluster,
                  use_seed=use_seed,
                  dist_weights=dist_weights,
                  n_calibration=n_calibration,
                  tolerance_quantile=tolerance_quantile,
                  proposal_phi=proposal_phi,
                  numcomp=numcomp,
                  seed_count=seed_count,
                  progress_bar=progress_bar)

  # generate random seeds
  seeds <- util_generate_seeds(nseeds)

  # Add simdesign to nl
  new_simdesign <- simdesign(simmethod="ABCmcmc",
                             siminput=tibble::tibble(),
                             simobject=abcmcmc,
                             simseeds=seeds)

  return(new_simdesign)

}