calculate_metrics.Rd
One use case for these metrics is to calculate the accuracy of a certain prediction compared to a reference trajectory. However, these metrics can also be used for other purposes, such as clustering of trajectories.
calculate_metrics(dataset, model, metrics = dyneval::metrics$metric_id, expression_source = dataset$expression)
dataset  The first trajectory, in most cases a gold standard trajectory 

model  The second trajectory, in most cases a predicted trajectory 
metrics  Which metrics to evaluate. Check 
expression_source  The expression data matrix, with features as columns.

Some metrics are asymmetric (see dyneval::metrics$symmetric
), in which case the order of the dataset and model parameters matters.