random_seed  

Seed used for generating random values. Using the same seed value, i.e. 123, ensures that running the same model, with the same data and settings generates the same result, despite stochastic processes such as machine learning or cross-validation. 

Number of repetitions (nb_run_eval) 

Integer value, corresponding to the number of repetitions to be done for calibration/validation splitting (default = 10) 

Data split percentage (data_split) 

Numeric value between 0 and 100, corresponding to the percentage of data used to calibrate the models (calibration/validation splitting) (default = 100) 

prevalence 

Allows to give more or less weight to particular observations; default = NULL: each observation (presence or absence) has the same weight; if value < 0.5: absences are given more weight; if value > 0.5: presences are given more weight. 

Variable importance (var_import) 

Integer value, corresponding to the number of permutations to be done for each variable to estimate variable importance (default = 0) 

Scale models (rescale_all_models) 

A logical value defining whether all models predictions should be scaled with a binomial GLM or not (default = FALSE) 

Evaluate all models (do_full_models) 

A logical value defining whether models calibrated and evaluated over the whole dataset should be computed or not (default = TRUE) 

Quantile (quant)  

The value defines the most extreme values for each variable not to be taken into account for determining the tolerance boundaries for the considered species. The default value is 0.025, which corresponds to a 95% confidence interval in normal distributions (default = 0.025)