| alt_delta | Rescorla-Wagner (Delta) Model |
| alt_gamma | Rescorla-Wagner (Gamma) Model |
| bandit2arm_delta | Rescorla-Wagner (Delta) Model |
| bandit4arm2_kalman_filter | Kalman Filter |
| bandit4arm_2par_lapse | 3 Parameter Model, without C (choice perseveration), R... |
| bandit4arm_4par | 4 Parameter Model, without C (choice perseveration) |
| bandit4arm_lapse | 5 Parameter Model, without C (choice perseveration) but with... |
| bandit4arm_lapse_decay | 5 Parameter Model, without C (choice perseveration) but with... |
| bandit4arm_singleA_lapse | 4 Parameter Model, without C (choice perseveration) but with... |
| banditNarm_2par_lapse | 3 Parameter Model, without C (choice perseveration), R... |
| banditNarm_4par | 4 Parameter Model, without C (choice perseveration) |
| banditNarm_delta | Rescorla-Wagner (Delta) Model |
| banditNarm_kalman_filter | Kalman Filter |
| banditNarm_lapse | 5 Parameter Model, without C (choice perseveration) but with... |
| banditNarm_lapse_decay | 5 Parameter Model, without C (choice perseveration) but with... |
| banditNarm_singleA_lapse | 4 Parameter Model, without C (choice perseveration) but with... |
| bart_ewmv | Exponential-Weight Mean-Variance Model |
| bart_par4 | Re-parameterized version of BART model with 4 parameters |
| cgt_cm | Cumulative Model |
| choiceRT_ddm | Drift Diffusion Model |
| choiceRT_ddm_single | Drift Diffusion Model |
| choiceRT_lba | Choice Reaction Time task, linear ballistic accumulator... |
| choiceRT_lba_single | Choice Reaction Time task, linear ballistic accumulator... |
| cra_exp | Exponential Subjective Value Model |
| cra_linear | Linear Subjective Value Model |
| dbdm_prob_weight | Probability Weight Function |
| dd_cs | Constant-Sensitivity (CS) Model |
| dd_cs_single | Constant-Sensitivity (CS) Model |
| dd_exp | Exponential Model |
| dd_hyperbolic | Hyperbolic Model |
| dd_hyperbolic_single | Hyperbolic Model |
| dot-hbayesdm_compile | Load (and compile if necessary) a CmdStanModel for an... |
| dot-hbayesdm_extract | Extract draws for the requested parameters from a cmdstanr... |
| dot-hbayesdm_fit | Fit an hBayesDM Stan model via cmdstanr. |
| dot-hbayesdm_resolve_inits | Resolve the 'inits' argument into the format cmdstanr... |
| dot-hbayesdm_stan_file | Locate the .stan file for a given hBayesDM model name. |
| estimate_mode | Function to estimate mode of MCMC samples |
| extract_ic | Extract Model Comparison Estimates |
| gng_m1 | RW + noise |
| gng_m2 | RW + noise + bias |
| gng_m3 | RW + noise + bias + pi |
| gng_m4 | RW (rew/pun) + noise + bias + pi |
| hbayesdm-cmdstan | Internal helpers for fitting hBayesDM Stan models via... |
| hBayesDM_model | hBayesDM Model Base Function |
| hBayesDM-package | Hierarchical Bayesian Modeling of Decision-Making Tasks |
| hdi | Compute Highest-Density Interval |
| hgf_ibrb | Hierarchical Bayesian version of the Hierarchical Gaussian... |
| hgf_ibrb_single | Individual-level Bayesian version of the Hierarchical... |
| igt_orl | Outcome-Representation Learning Model |
| igt_pvl_decay | Prospect Valence Learning (PVL) Decay-RI |
| igt_pvl_delta | Prospect Valence Learning (PVL) Delta |
| igt_vpp | Value-Plus-Perseverance |
| multiplot | Function to plot multiple figures |
| peer_ocu | Other-Conferred Utility (OCU) Model |
| plot_dist | Plots the histogram of MCMC samples. |
| plot.hBayesDM | General Purpose Plotting for hBayesDM. This function plots... |
| plot_hdi | Plots highest density interval (HDI) from (MCMC) samples and... |
| plot_ind | Plots individual posterior distributions using 'bayesplot'. |
| print_fit | Print model-fits (mean LOOIC or WAIC values in addition to... |
| prl_ewa | Experience-Weighted Attraction Model |
| prl_fictitious | Fictitious Update Model |
| prl_fictitious_multipleB | Fictitious Update Model |
| prl_fictitious_rp | Fictitious Update Model, with separate learning rates for... |
| prl_fictitious_rp_woa | Fictitious Update Model, with separate learning rates for... |
| prl_fictitious_woa | Fictitious Update Model, without alpha (indecision point) |
| prl_rp | Reward-Punishment Model |
| prl_rp_multipleB | Reward-Punishment Model |
| pst_gainloss_Q | Gain-Loss Q Learning Model |
| pst_Q | Q Learning Model |
| pstRT_ddm | Drift Diffusion Model |
| pstRT_rlddm1 | Reinforcement Learning Drift Diffusion Model 1 |
| pstRT_rlddm6 | Reinforcement Learning Drift Diffusion Model 6 |
| ra_noLA | Prospect Theory, without loss aversion (LA) parameter |
| ra_noRA | Prospect Theory, without risk aversion (RA) parameter |
| ra_prospect | Prospect Theory |
| rdt_happiness | Happiness Computational Model |
| rhat | Function for extracting Rhat values from an hBayesDM object |
| task2AFC_sdt | Signal detection theory model |
| ts_par4 | Hybrid Model, with 4 parameters |
| ts_par6 | Hybrid Model, with 6 parameters |
| ts_par7 | Hybrid Model, with 7 parameters (original model) |
| ug_bayes | Ideal Observer Model |
| ug_delta | Rescorla-Wagner (Delta) Model |
| wcs_sql | Sequential Learning Model |
