dRiftDM 0.3.2

dRiftDM 0.3.2

Minor

• Bayesian hierarchical estimation is now fully reproducable when using the seed argument of estimate_dm().

Bug fix

• Ensure EZ guesses are within the search space

dRiftDM 0.3.1

Minor

• For type = "sep_b" in estimate_dm(): Chains are now initialized more robustly.

• We reverted back to adding the value 0.001 to t_vec when evaluating DMC’s drift rate for a!=2 (after noticing solver crashes with the former value of 1e-5).

• It is now possible to pass a value via ddm_opts() to objects of type dmc_dm(); this controls the value that is added to t_vec when evaluating DMC’s drift rate for a!=2.

dRiftDM 0.3.0

Important notes for users

• The workflow for estimating a model has changed with the introduction of estimate_dm(). From this version onward, fits are not automatically saved to the file system to be reloaded later. Instead, estimate_dm() returns fitted objects directly, and you save the results yourselves.

Major and new features

• dRiftDM now uses an adaptive time-stepping scheme for deriving PDFs — substantially increasing speed.

• We now support variability in the drift rate for the constant drift-rate component (not only for the Ratcliff DDM).

• plot() methods were redesigned to avoid argument clashes and to provide more customization options.

• drift_dm() objects gain a new entry cost_function. This lets us use the "rmse" statistic or full-range maximum likelihood, and it enables fitting aggregated data via "rmse".

• The neg_log_like entry of a drift_dm object has been replaced by the more general cost_value.

• cost_function() accessor and replacement methods have been introduced.

• cost_value() accessor and replacement methods have been introduced.

• estimate_dm() has been introduced.

• If possible, dRiftDM now provides reasonable starting values for the Nelder-Mead and BFGS optimization routines (both bounded and unbounded). To this end, EZ Diffusion parameter estimates are used whenever possible, in combination with grid-search-like procedure.

• estimate_model() has been deprecated and superseded by estimate_dm().

• estimate_model_ids() has been deprecated. Use estimate_dm(), which does not save individual fits to the file system — ensuring more consistent behavior across fitting modes.

• get_lower_upper() has been introduced. It provides default upper and lower parameter ranges for pre-built models and their components.

• Hierarchical and non-hierarchical Bayesian parameter estimation is now possible via estimate_dm()! This is still experimental, and the returned mcmc_dm type is not fully integrated yet (currently: diagnostic checks and parameter extraction is supported).

• calc_stats() gains basic_stats and densities options for type. basic_stats returns means, standard deviations, and choice proportions; densities returns density values.

• calc_stats() gains a resample option to quantify variability in model predictions. We can resample for a given model or a single individual, or bootstrap an entire sample.

• calc_stats() arguments split_by_ID and average have been superseded by the more general level argument.

• simulate_data() no longer returns RTs restricted to the time grid (step size dt). PDFs are linearly interpolated for inverse transform sampling. We can control RT decimal places via round_to.

• simulate_data() now supports the conds argument.

• ssp_dm() gains var_non_dec and var_start to toggle variability in non-decision time and starting point.

• ssp_dm() now uses uniform variability in non-decision time, aligning more closely with the original publication.

• ssp_dm() default dx and dt increase computation speed while balancing numerical error for many parameter values.

• dmc_dm() default dx and dt increase computation speed while balancing numerical error for many parameter values.

• ratcliff_dm() default dx and dt increase computation speed while balancing numerical error for many parameter values.

• coef() and plot() now support the mcmc_dm object type.

• check_discretization() has been introduced. This function helps us assess the loss in precision when increasing dt and dx.

• get_example_fits_ids() was removed.

• get_example_fits() has been introduced to obtain fits_ids_dm, fits_agg_dm, or mcmc_dm objects.

Minor

• nt_constant() now uses round() instead of as.integer() to locate the Dirac delta index, reducing bias in non-decision time estimates.

• pdfs() now also returns a vector of the time domain.

• The coef() and plot() method now supports mcmc_dm objects.

• The progress argument replaces verbose in calc_stats (default: 1).

• The "fit_stats" option for calc_stats() now returns multiple fit statistics, including log-likelihood, AIC, BIC, and root-mean-squared error.

• simulate_traces now properly considers trial-by-trial variability in the drift rate.

Bug and code fixes

• calc_stats() is now more precise due to proper numerical integration.

dRiftDM 0.2.2

New features:

• print() and summary() methods are now available for traces_dm, traces_dm_list, stats_dm, stats_dm_list, and coefs_dm objects.

• New unpack_obj() makes it easy to strip away attributes and class labels of objects created by dRiftDM. The more specific predecessor function unpack_traces() is now deprecated unpack_traces().

• New pdfs() provides access to a model’s predicted probability density function.

Minor improvements and changes:

• coef.drift_dm() gains a select_custom_prms argument.

• list_stats_dm objects are now called stats_dm_list for name consistency.

• traces_dm objects now have additional attributes (which were required for appropriate print and summary methods).

• More consistent capitalization in print.summary.*() methods.

dRiftDM 0.2.1

• Initial CRAN submission.