Released: June 19, 2020
New features
- Trial-wise trajectory support for Overlays
Overlays are an important feature of the GDDM, but previously they
were not supported on trial-wise trajectory simulations. Now, it
is possible to define the function "apply_trajectory" in the
Overlay object if the overlay can be applied to a trajectory
simulation.
- Details of model fits are preserved
After running a model fit, it is useful to know the parameters of
the fit, the objective function value, the methods used for the
fit, etc. This information is now easily-accessible from within
fit models as a FitResult object, with clear documentation on how
to use it.
- Performance enhancements for Crank-Nicolson and analytical solutions
Moderate speedups for the Crank-Nicolson method, and a roughly one
order of magnitude speedup for analytical solutions
- Samples can be exported
Previously, it was possible to create a new Sample object from a
pandas DataFrame. Now, it is also possible to do the reverse, i.e.
convert an existing Sample object to a pandas DataFrame.
Bug fixes
- Documentation links now refer to the stable version instead of the
development version.
- Fixed bug when bounds collapse to zero
- Sample objects were internally inconsistent when imported through
pandas
Other
- New option to suppress diagnostic text (thanks Arkady!)
- LossRobustBIC and LossRobustLikelihood now provide shortcuts for
uniform distribution overlays.
- Added a new implementation of biased reward for compatibility with
fittable bounds (thanks Nathan!)
- get_model_loss function as a shortcut for finding the value of a
given loss function for a given model
- New function to get list of model parameter names
- Function to compute mean decision time for samples
- More informative error messages
Breaking changes
- The command-line argument "method" now has a different meaning, and
will throw an error if used for the previous purpose. This made
terminology for "fitting_method" and "method" more consistent: now
"fitting_method" refers to the optimization routine
(e.g. differential evolution) whereas "method" refers to the
numerical algorithm (e.g. backward Euler)
- The "returnEvolution" arguement is now called "return_evolution".