Function reference • hBayesDM

Tasks & Models
`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 (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
`bandit4arm_4par()`	4 Parameter Model, without C (choice perseveration)
`bandit4arm_lapse()`	5 Parameter Model, without C (choice perseveration) but with xi (noise)
`bandit4arm_lapse_decay()`	5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
`bandit4arm_singleA_lapse()`	4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
`banditNarm_2par_lapse()`	3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
`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 xi (noise)
`banditNarm_lapse_decay()`	5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).
`banditNarm_singleA_lapse()`	4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
`bart_ewmv()`	Exponential-Weight Mean-Variance Model
`bart_par4()`	Re-parameterized version of BART model with 4 parameters
`choiceRT_ddm()`	Drift Diffusion Model
`choiceRT_ddm_single()`	Drift Diffusion Model
`cgt_cm()`	Cumulative Model
`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
`gng_m1()`	RW + noise
`gng_m2()`	RW + noise + bias
`gng_m3()`	RW + noise + bias + pi
`gng_m4()`	RW (rew/pun) + noise + bias + pi
`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
`peer_ocu()`	Other-Conferred Utility (OCU) Model
`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 positive and negative prediction error (PE)
`prl_fictitious_rp_woa()`	Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE), without alpha (indecision point)
`prl_fictitious_woa()`	Fictitious Update Model, without alpha (indecision point)
`prl_rp()`	Reward-Punishment Model
`prl_rp_multipleB()`	Reward-Punishment Model
`pst_Q()`	Q Learning Model
`pst_gainloss_Q()`	Gain-Loss 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
`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
Diagnostics
`estimate_mode()`	Function to estimate mode of MCMC samples
`extract_ic()`	Extract Model Comparison Estimates
`HDIofMCMC()`	Compute Highest-Density Interval
`multiplot()`	Function to plot multiple figures
`plotDist()`	Plots the histogram of MCMC samples.
`plotHDI()`	Plots highest density interval (HDI) from (MCMC) samples and prints HDI in the R console. HDI is indicated by a red line. Based on John Kruschke's codes.
`plotInd()`	Plots individual posterior distributions, using the stan_plot function of the rstan package
`printFit()`	Print model-fits (mean LOOIC or WAIC values in addition to Akaike weights) of hBayesDM Models
`rhat()`	Function for extracting Rhat values from an hBayesDM object

Reference

Tasks & Models

Diagnostics