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hBayesDM 1.2.1

• Fixed a pkgdown error.

hBayesDM 1.2.0

• Added a drift diffusion model and two reinforcement learning-drift diffision models for the probabilistic selection task: pstRT_ddm, pstRT_rlddm1, and pstRT_rlddm6.
• Added multiple models for the banditNarm task: banditNarm_2par_lapse, banditNarm_4par, banditNarm_delta, banditNarm_kalman_filter, banditNarm_lapse, banditNarm_lapse_decay, and banditNarm_singleA_lapse.
• Fixed bart_ewmv to avoid dividing by zero.

hBayesDM 1.1.1

• Fix symbolic link errors for stan files and example data.

hBayesDM 1.1.0

• Added the cumulative model for the Cambridge gambling task: cgt_cm.
• Added two new models for aversive learning tasks: alt_delta and alt_gamma.
• Added exponential-weight mean-variance model for BART task: bart_ewmv.
• Added simple Q learning model for the probabilistic selection task: prl_Q.
• Added signal detection theory model for 2-alternative forced choice task: task2AFC_sdt.

hBayesDM 1.0.2

• Fixed an error on using data.frame objects as data (#112).

hBayesDM 1.0.1

• Minor fix on the plotting function.

hBayesDM 1.0.0

Major changes

• Now, hBayesDM has both R and Python version, with same models included! You can run hBayesDM with a language you prefer!
• Models in hBayesDM are now specified as YAML files. Using the YAML files, R and Python codes are generated automatically. If you want to contribute hBayesDM by adding a model, what you have to do is just to write a Stan file and to specify its information! You can find how to do in the hBayesDM wiki (https://github.com/CCS-Lab/hBayesDM/wiki).
• Model functions try to use parameter estimates using variational Bayesian methods as its initial values for MCMC sampling by default (#96). If VB estimation fails, then it uses random values instead.
• The data argument for model functions can handle a data.frame object (#2, #98).
• choiceRT_lba and choiceRT_lba_single are temporarily removed since their codes are not suitable to the new package structure. We plan to re-add the models in future versions.
• The Cumulative Model for Cambridge Gambling Task is added (cgt_cm; #108).

Minor changes

• The tau parameter in all models for the risk aversion task is modified to be bounded to [0, 30] (#77, #78).
• bart_4par is fixed to compute subject-wise log-likelihood (#82).
• extract_ic is fixed for its wrong rep function usage (#94, #100).
• The drift rate (delta parameter) in choiceRT_ddm and choiceRT_ddm_single is unbounded and now it is estimated between [-Inf, Inf] (#95, #107).
• Fix a preprocessing error in choiceRT_ddm and choiceRT_ddm_single (#95, #109).
• Fix igt_orl for a wrong Matt trick operation (#110).

hBayesDM 0.7.2

• Add three new models for the bandit4arm task: bandit4arm_2par_lapse, bandit4arm_lapse_decay and bandit4arm_singleA_lapse.
• Fix various (minor) errors.

hBayesDM 0.7.1

• Make it usable without manually loading rstan.
• Remove an annoying warning about using ..insensitive_data_columns.

hBayesDM 0.7.0

• Now, in default, you should build a Stan file into a binary for the first time to use it. To build all the models on installation, you should set an environmental variable BUILD_ALL to true before installation.
• Now all the implemented models are refactored using hBayesDM_model function. You don’t have to change anything to use them, but developers can easily implement new models now!
• We added a Kalman filter model for 4-armed bandit task (bandit4arm2_kalman_filter; Daw et al., 2006) and a probability weighting function for general description-based tasks (dbdm_prob_weight; Erev et al., 2010; Hertwig et al., 2004; Jessup et al., 2008).
• Initial values of parameter estimation for some models are updated as plausible values, and the parameter boundaries of several models are fixed (see more on issue #63 and #64 in Github).
• Exponential and linear models for choice under risk and ambiguity task now have four model regressors: sv, sv_fix, sv_var, and p_var.
• Fix the Travix CI settings and related codes to be properly passed.

hBayesDM 0.6.3

• Update the dependencies on rstan (>= 2.18.1)
• No changes on model files, as same as the version 0.6.2

hBayesDM 0.6.2

• Fix an error on choiceRT_ddm (#44)

hBayesDM 0.6.1

• Solve an issue with built binary files.
• Fix an error on peer_ocu with misplaced parentheses.

hBayesDM 0.6.0

• Add new tasks (Balloon Analogue Risk Task, Choice under Risk and Ambiguity Task, Probabilistic Selection Task, Risky Decision Task (a.k.a. Happiness task), Wisconsin Card Sorting Task)
• Add a new model for the Iowa Gambling Task (igt_orl)
• Change priors (Half-Cauchy(0, 5) –> Half-Cauchy(0, 1) or Half-Normal(0, 0.2)
• printFit function now provides LOOIC weights and/or WAIC weights

hBayesDM 0.5.1

• Add models for the Two Step task
• Add models without indecision point parameter (alpha) for the PRL task (prl_*_woa.stan)
• Model-based regressors for the PRL task are now available
• For the PRL task & prl_fictitious.stan & prl_fictitious_rp.stan –> change the range of alpha (indecision point) from [0, 1] to [-Inf, Inf]

hBayesDM 0.5.0

• Support variational Bayesian methods (vb=TRUE)
• Allow posterior predictive checks, except for drift-diffusion models (inc_postpred=TRUE)
• Add the peer influence task (Chung et al., 2015, USE WITH CAUTION for now and PLEASE GIVE US FEEDBACK!)
• Add ‘prl_fictitious_rp’ model
• Made changes to be compatible with the newest Stan version (e.g., // instead of # for commenting).
• In ’prl_*’ models, ‘rewlos’ is replaced by ‘outcome’ so that column names and labels would be consistent across tasks as much as possible.
• Email feature is disabled as R mail package does not allow users to send anonymous emails anymore.
• When outputs are saved as a file (*.RData), the file name now contains the name of the data file.

hBayesDM 0.4.0

• Add a choice reaction time task and evidence accumulation models
  • Drift diffusion model (both hierarchical and single-subject)
  • Linear Ballistic Accumulator (LBA) model (both hierarchical and single-subject)
• Add PRL models that can fit multiple blocks
• Add single-subject versions for the delay discounting task (dd_hyperbolic_single and dd_cs_single).
• Standardize variable names across all models (e.g., rewlos –> outcome for all models)
• Separate versions for CRAN and GitHub. All models/features are identical but the GitHub version contains precompilled models.

hBayesDM 0.3.1

• Remove dependence on the modeest package. Now use a built-in function to estimate the mode of a posterior distribution.
• Rewrite the “printFit” function.

hBayesDM 0.3.0

• Made several changes following the guidelines for R packages providing interfaces to Stan.
• Stan models are precompiled and models will run immediately when called.
• The default number of chains is set to 4.
• The default value of adapt_delta is set to 0.95 to reduce the potential for divergences.
• The “printFit” function uses LOOIC by default. Users can select WAIC or both (LOOIC & WAIC) if needed.

hBayesDM 0.2.3.3

• Add help files
• Add a function for checking Rhat values (rhat).
• Change a link to its tutorial website

hBayesDM 0.2.3.2

• Use wide normal distributions for unbounded parameters (gng_* models).
• Automatic removal of rows (trials) containing NAs.

hBayesDM 0.2.3.1

• Add a function for plotting individual parameters (plotInd)

hBayesDM 0.2.3

• Add a new task: the Ultimatum Game
• Add new models for the Probabilistic Reversal Learning and Risk Aversion tasks
• ‘bandit2arm’ -> change its name to ‘bandit2arm_delta’. Now all model names are in the same format (i.e., TASK_MODEL).
• Users can extract model-based regressors from gng_m* models
• Include the option of customizing control parameters (adapt_delta, max_treedepth, stepsize)
• ‘plotHDI’ function -> add ‘fontSize’ argument & change the color of histogram

hBayesDM 0.2.1

Bug fixes

• All models: Fix errors when indPars=“mode”
• ra_prospect model: Add description for column names of a data (*.txt) file

Change

• Change standard deviations of ‘b’ and ‘pi’ priors in gng_* models

hBayesDM 0.2.0

Initially released.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.