Result: A value from the binomial distribution with the given probability and number of trials. A new random deviate is generated each time step.
The binomial distribution describes the probability of a given number of positive outcomes occurring when a number n of trials are carried out, each with a certain probability p of a positive outcome.
Result: A value from the Poisson distribution with the given mean. A new random deviate is generated each time step.
The poisson distribution describes the probability of a given number of positive outcomes occurring in the limiting case of the binomial distribution, i.e., with very many trials each with a very small chance of a positive outcome.
Result: A random sample from a Gaussian (normal) distribution, with the supplied mean and standard deviation. A new random sample is generated each time step.
This function is implemented using a pseudo-random sequence generator; notes regarding its behaviour can be found in the documentation for the rand_var function.
Returns the value the argument had when first used, i.e., on model reset or when the submodel instance containing this equation was created.
at_init(X) creates an implicit intermediate result, which has the same dimensions as its argument. So if this result is implicitly replicated elsewhere in the equation, the same value will be used each time. See makearray for behaviour in explicit replication.
Takes an array of numeric values, and returns an array with the ranks of the corresponding elements in the argument. This is 1 for the largest element, and equal to the size of the array for the smallest.
