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 the symlink error in the Python version due to releasing with poetry
• Fix minor errors in both R and Python

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

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

hBayesDM 1.0.1

• Minor fix on R and Python codes (R, #111).

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 model 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.