Major refactor; breaking changes.
Backend swap: rstan → cmdstanr
- The Stan backend is now CmdStan via cmdstanr, replacing rstan. CmdStan is a system dependency, not a CRAN package — install it once with
cmdstanr::install_cmdstan() after installing cmdstanr from the Stan r-universe (see README).
- The
fit slot on the result object is now a CmdStanMCMC object (or CmdStanVB when vb = TRUE), no longer a rstan::stanfit. Methods that used to apply directly (e.g. rstan::extract(fit)) need to be replaced with the cmdstanr / posterior equivalents — fit$draws(), fit$summary(), posterior::as_draws_*(), etc.
- Stan files have been canonicalized to the modern syntax (
array[N, T] real x instead of real x[N, T]; abs replacing fabs).
-
R ≥ 4.4 is now required.
- hBayesDM no longer compiles Stan models at install time. Each model compiles on first use (~30 s) and cmdstanr caches the binary for subsequent fits. The
BUILD_ALL install-time flag is gone.
-
LinkingTo: rstan, StanHeaders, ... and the C++ machinery they entailed have been removed from the package.
API changes
-
rhat() — internally uses posterior::rhat via fit$summary() (a workaround for a name-collision bug between hBayesDM::rhat and the string "rhat" looked up by cmdstanr::CmdStanFit$summary()).
-
extract_ic() — now extracts log_lik from fit$draws() via posterior::as_draws_array(). The ic = "looic" | "waic" | "both" API is unchanged.
-
plot.hBayesDM() and plot_ind() — now plot via bayesplot (mcmc_trace, mcmc_intervals, mcmc_areas) instead of rstan::stan_plot.
-
Breaking — snake_case sweep. The R public API is now fully snake_case, matching modern R style (tidyverse / Google guides) and the Python package character-for-character. User-visible renames:
- Functions:
printFit → print_fit, plotHDI → plot_hdi, plotInd → plot_ind, plotDist → plot_dist, HDIofMCMC → hdi.
- Arguments:
modelRegressor → model_regressor, indPars → ind_pars, credMass → ci_prob (on hdi() and plot_hdi()), fontSize → font_size, xLab → x_lab, yLab → y_lab, xLim → x_lim, binSize → bin_size, sampleVec → sample_vec, roundTo → round_to, Title → title.
- Result-object slots:
$allIndPars → $all_ind_pars, $parVals → $par_vals, $modelRegressor → $model_regressor, $rawdata → $raw_data. ($model and $fit unchanged.)
-
HDI default credible mass.
hdi() and plot_hdi() default ci_prob = 0.95 (unchanged on the R side). The Python package now also defaults to 0.95 (previously 0.94, inherited from arviz) so the two languages produce matching HDI bands out of the box.
-
additional_args plumbing fixed: model wrappers that declared a NULL default (e.g. banditNarm_2par_lapse Narm, pstRT_ddm initQ) were silently dropping it because args[[nm]] <- NULL removes a list element in R; now uses args[nm] <- list(NULL) to preserve the entry.
Migration notes
If you have downstream code that calls rstan::extract(output$fit), replace it with one of:
posterior::as_draws_df(output$fit$draws()) # tidy data.frame
posterior::as_draws_rvars(output$fit$draws())
output$par_vals[["mu_k"]] # already-extracted samples
- Add plot functions for Hierarchical Gaussian Filter models:
plot_hgf_ibrb, plot_hgf_ibrb_single.
- 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.
- Fix symbolic link errors for stan files and example data.
- 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.
- Fixed an error on using data.frame objects as data (#112).
- Minor fix on the plotting function.
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).
- Add three new models for the bandit4arm task:
bandit4arm_2par_lapse, bandit4arm_lapse_decay and bandit4arm_singleA_lapse.
- Fix various (minor) errors.
- Make it usable without manually loading
rstan.
- Remove an annoying warning about using
..insensitive_data_columns.
- 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.
- Update the dependencies on rstan (>= 2.18.1)
- No changes on model files, as same as the version 0.6.2
- Fix an error on choiceRT_ddm (#44)
- Solve an issue with built binary files.
- Fix an error on peer_ocu with misplaced parentheses.
- 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
- 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]
- 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.
- 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.
- Remove dependence on the modeest package. Now use a built-in function to estimate the mode of a posterior distribution.
- Rewrite the “printFit” function.
- 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.
- Add help files
- Add a function for checking Rhat values (rhat).
- Change a link to its tutorial website
- Use wide normal distributions for unbounded parameters (gng_* models).
- Automatic removal of rows (trials) containing NAs.
- Add a function for plotting individual parameters (plotInd)
- 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
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