HDIofMCMC               Compute Highest-Density Interval
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.
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
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
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-package        Hierarchical Bayesian Modeling of
                        Decision-Making Tasks
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.hBayesDM           General Purpose Plotting for hBayesDM. This
                        function plots hyper parameters.
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
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_gainloss_Q          Gain-Loss Q Learning Model
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
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
