Pyddm

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".

Released: October 20, 2019

New features

- Increasing bounds

Previously, PyDDM supported collapsing bounds, but bounds were
required to be monotonically decreasing. This restriction was
lifted, allowing bounds to both increase and decrease over time
according to any arbitrary function.

- Tracking decision variable evolution (thanks Stefan!)

The evolution of the decision variable distribution may be tracked
over time by passing a flag to the solver routine.

- Custom optimizers

Previously, PyDDM required optimizers (e.g. differential evolution
or the Nelder-Mead simplex algorithm) to be hard-coded. Now,
these algorithms can be passed as arguments to the
"fit_adjust_model" function.

- Improvements in the model GUI

The model GUI now has nicer-looking plots, and is more transparent
when errors occur in the model. Additionally, it now supports
plotting a model when a sample is not present.

Bug fixes

- Under certain conditions, parallel simulations were computationally
inefficient.
- ICPoint had invalid arguments for the get_IC function
- simulate_trial cut off when reaching the boundary, contrary to the
function documentation

Other

- The PyDDM cookbook now provides several simple examples of how to
use PyDDM in a variety of situations.
- Particle simulations may now use either RK4 or Euler's method
- Several new model components were added
- Error messages were improved