- Added functionality to perform modelling using the classic drift diffusion model (see here)
- set ‘model=“DDM_classic”’ in DDModel
- In addition to the above the lexical decision and repetition memory task were introduced to the package (only for DDM_classic for now)
- set ‘task=“RMT_LDT”’ in DDModel
- DDRep now has the ability to reshape a given DDrep between different representations
- Specify ‘ddrep’ in DDRep (Note: Only reshaping inside an identical DDModel framework is allowed!)
- Added a handy function for extracting scaling using Deep Learning models (see
Scale_DL_Data())
- Several performance improvements
DDModeling 0.0.1.4
- Several performance improvements (especially to GRID_Import)
- Added ability to tune SIMPLEX parameters in Fit_DDModel (see new argument simplex_coef)
- Added a Compare generic method to DDRep
DDModeling 0.0.1.3
- Added multi thread support to several functions!
- Defaults to k-1 threads where k is the maximum number of threads available to the system
- Added functionality for fitting with deep learning methods in Fit_DDModel
- Added functionality to easily convert GRIDs into datasets suitable for the training of neural networks using deep learning methods
DDModeling 0.0.1.2
- Optimizations
- Added reference methods to DDFit objects:
- plot: plots CAF and CDF distributions for a given DDFit object
- summary: displays eta values (see here) and booth input and fitted parameters
- Added an import function for GRIDs: Import_GRID
- Added fitting structure customization to Fit_DDModel through a new parameter ‘symplex_struc’
DDModeling 0.0.1.1
- Minor restructuring of some classes in order to increase efficiency
- Added information to several documentations
- Added functionality to the Sim_DDModel function:
- ability to calculate multiple simulations
- ability to initialize a simulation with manually chosen parameters
First package setup, including:
- Full support for three models: DSTP, DMC, SSP
- Additional support for customization of each model listed above
- Ability to generate
- model predictions
- model grids
- CDF/CAF representations from data
- Ability to fit data using a combination of grid-search and downhill simplex (as described here)