Adjustment to default priors#159
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These changes adjust the default priors for the "top" and "bottom" parameters. Usage of the package has shown the priors to be too weakly informative, and recent evidence suggests that tighter regularizing priors can produce much better results, particularly in terms of speeding up convergence. Remember, several tools are available to examine your priors and these should be used to check their adequacy:
https://open-aims.github.io/bayesnec/articles/example3.html
Changes are shown in the screenshot below:
The main focus of the changes were to move them closer to the maximum (for top) an minimum (for bottom) observed values in the response data, and to slightly tighten them around those values.