Study and visualize simulation output dynamics, namely the range of values per iteration and the existence or otherwise of transient and steady state stages.

Perform distributional analysis of focal measures (FMs), i.e. of statistical summaries taken from model outputs (e.g., maximum, minimum, steady state averages).

Determine the alignment of two or more model implementations by statistically comparing FMs. In other words, aid in the process of

From the previous points, produce publication quality LATEX tables and figures (the latter via the

These utilities were originally developed to study the Predator-Prey for High-Performance Computing (PPHPC) agent-based model [

The

Core functions.

Distributional analysis functions.

Model comparison functions.

Helper and third-party functions (not shown in Figure

The next sections describe each group of functions in additional detail.

Core functions work directly with simulation output files or perform low-level manipulation of outputs. The

Two

The

The matrix returned by

Plots of simulation output from one or more replications can be produced using

Types of plot provided by the

The provided

Functions in the distributional analysis module generate tables and figures which summarize different aspects of the statistical distributions of FMs. The

The

The

Utilities in the model comparison group aid the modeler in comparing and aligning simulation models through informative tables and plots, also producing publication quality LATEX tables containing

The

The

There are two additional groups of functions, the first containing helper functions, and the second containing third-party functions.

Helper functions are responsible for tasks such as determining confidence intervals, histogram edges, QQ-plot points, moving averages and whether MATLAB or Octave is being used. Functions for formatting real numbers and

A number of third-party functions, mostly providing plotting features, are also included. The

All functions have been individually tested for correctness in both MATLAB and Octave, and most are covered by unit tests in order to ensure their correct behavior. The

Issues or bugs can be filed at

Any system capable of running MATLAB R2013a or GNU Octave 3.8.1, or higher.

MATLAB R2013a or GNU Octave 3.8.1, or higher.

MATLAB requires the Statistics Toolbox.

The software was created by Nuno Fachada.

English

These utilities can be used for analyzing any stochastic simulation model with time series-like outputs. As described in ‘Core functions‘, output-specific FMs can be defined by implementing a custom

This software uses additional MATLAB/Octave functions written by Chad A. Greene [

The authors declare that they have no competing interests.