This freely shared Matlab package will allow you to fit a number of non-linear functions to data. Data is grouped by any number of grouping factors, and separate fits are conducted (and saved) within each group. Currently the software can estimate fits for the following functions. - The **logistic** function. This function starts at one asymptote with a smooth nonlinear transition to a second asymptote. This is a four parameter function with separate parameters for each asymptote, the location of the crossover and the slope at the crossover. This is commonly used for Visual World Paradigm eye-tracking data (looks to the target), for psychophysical thresholds and for categorization paradigms from along a continuum. - The **assymetric/double Gaussian** function is commonly used for Visual World Paradigm data for looks to the competitor. This function starts from one asymptote, transitions to a peak, and then falls to a second asymptote. It has 6 free parameters: the onset and offset asymptotes, the location of the peak, the height of the peak and the onset and offset slopes. Note that these two are fit with a constrained fitting technique that limits the function values to lie between 0 and 1 (since these applications are usually probabilities). - **Power** and **Exponential functions** used to model reaction times in learning experiments. - The **rotated logistic** function used to model two dimensional psychophysical data when cue weighting and slope must be estimated separately. See Kanoula, Winn, Kong, Edwards and McMurray (2017) for an example. - The **exGaussian** distribution commonly used to assess RTs. All code requires the optimization toolbox. It is a work in progress and there may be bugs. No support is guaranteed. For complete documentation see the documentation that is included with the package.