Since algorithm files are text files, you have the convenience to edit them (carefully) using a text editor such as Notepad, though interactive editing using 'Edit algorithm data' is typically more efficient.

Once a particular model has been initialized, you will be required to load an algorithm file.

You may choose to have SmartStats © create a new, default, file (generally with all parameter values and step sizes equal to zero, and values unfused, or you may load an already existing file.

Note that among existing algorithm files, only those having the correct number of parameters for the initialized model will be read by SmartStats ©, however a default file will be set to the correct number of parameters for that model.

After clicking on 'Edit algorithm file' you can change parameter values, their step sizes, and fuse (or link) the values of parameters.

Note that I use the term 'fuse' to avoid confusion with the typical use of the term link in the context of a link function.

Note that parameters with a non-zero step size will be estimated, otherwise they will be assumed to be fixed at the specified parameter value.

The value of a non-zero step size is relevant in only two contexts.

The first context applies when using the 'simplex algorithm' to estimate parameter values.

In this context the step size is used to calculate the initial deviations of trial parameter values from their starting values when undergoing this geometric search for improved parameter values.

The second context applies when numerically calculating a covariance matrix of the estimated parameter values.

In this context the step sizes are used to scale the deviations of the parameter values from their maximum-likelihood values during numerical calculation of the first and second derivatives of the likelihood with respect to the parameter values.

I rarely adjust step sizes, almost always leaving them at 10% of the parameter value.

However, an experienced analyst may recognize situations where tweaking the step sizes can have a beneficial effect on parameter estimation or calculation of the covariance matrix.

Parameter values that you wish to share a common value are fused by co-associating them using either a one (e.g., 'A') or two (e.g., 'BB' or 'CZ') character alphabetical code.

Fused parameter will be co-estimated and the step sizes of all but the first in ordinal rank of the fused parameters will be fixed at zero (0).

Parameter names (descriptions) cannot be changed by an analyst, they are part of the compiled model code.

Other algorithm file options available are either self-explanatory or typically unimportant for most analyses, e.g., I have never modified the 'Step-size reduction factor'.

I have found that for maximum-likelihood estimation the 'Tolerance for Convergence' is reliably set to ABS (absolute) with a convergence tolerance of 0.0001.

A useful reference for further appreciating algorithm files is:

Mittertreiner, A., and J. Schnute. 1985. Simplex: a manual and software package for easy nonlinear parameter estimation and interpretation in fishery research. Canadian Technical Report of Fisheries and Aquatic Science 1384, Ottawa, Ontario, Canada.

Though most of the implementation aspects of this technical report are out-of-date, perhaps even obsolete, the conceptual descriptions of the simplex method, algorithm files, and covariance calculations are informative.