Package: hBayesDM 2.0.1

Woo-Young Ahn

hBayesDM: Hierarchical Bayesian Modeling of Decision-Making Tasks

Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding (Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.

Authors:Woo-Young Ahn [aut, cre], Nate Haines [aut], Lei Zhang [aut], Jinwoo Jeong [ctb], Harhim Park [ctb], Jaeyeong Yang [ctb], Jethro Lee [ctb]

hBayesDM_2.0.1.tar.gz
hBayesDM_2.0.1.zip(r-4.7-any)hBayesDM_2.0.1.zip(r-4.6-any)hBayesDM_2.0.1.zip(r-4.5-any)
hBayesDM_2.0.1.tgz(r-4.6-any)hBayesDM_2.0.1.tgz(r-4.5-any)
hBayesDM_2.0.1.tar.gz(r-4.7-any)hBayesDM_2.0.1.tar.gz(r-4.6-any)
hBayesDM_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hBayesDM/json (API)

# Install 'hBayesDM' in R:
install.packages('hBayesDM', repos = c('https://ccs-lab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ccs-lab/hbayesdm/issues

On CRAN:

Conda:

bayesiancomputationaldecision-makinghierarchical-bayesian-analysismodelingreinforcement-learning

9.74 score 265 stars 464 scripts 632 downloads 18 mentions 73 exports 46 dependencies

Last updated from:ffe2f1c5d3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK186
source / vignettesOK246
linux-releaseOK185
macos-releaseOK246
macos-oldrelOK228
windows-develOK153
windows-releaseOK134
windows-oldrelOK137
wasm-releaseOK140

Exports:alt_deltaalt_gammabandit2arm_deltabandit4arm_2par_lapsebandit4arm_4parbandit4arm_lapsebandit4arm_lapse_decaybandit4arm_singleA_lapsebandit4arm2_kalman_filterbanditNarm_2par_lapsebanditNarm_4parbanditNarm_deltabanditNarm_kalman_filterbanditNarm_lapsebanditNarm_lapse_decaybanditNarm_singleA_lapsebart_ewmvbart_par4cgt_cmchoiceRT_ddmchoiceRT_ddm_singlecra_expcra_lineardbdm_prob_weightdd_csdd_cs_singledd_expdd_hyperbolicdd_hyperbolic_singleestimate_modeextract_icgng_m1gng_m2gng_m3gng_m4hdihgf_ibrbhgf_ibrb_singleigt_orligt_pvl_decayigt_pvl_deltaigt_vppmultiplotpeer_ocuplot_distplot_hdiplot_indprint_fitprl_ewaprl_fictitiousprl_fictitious_multipleBprl_fictitious_rpprl_fictitious_rp_woaprl_fictitious_woaprl_rpprl_rp_multipleBpst_gainloss_Qpst_QpstRT_ddmpstRT_rlddm1pstRT_rlddm6ra_noLAra_noRAra_prospectrdt_happinessrhattask2AFC_sdtts_par4ts_par6ts_par7ug_bayesug_deltawcs_sql

Dependencies:abindbackportsbayesplotcheckmateclicpp11data.tabledistributionaldplyrfarvergenericsggdistggplot2ggridgesgluegtableisobandlabelinglifecycleloomagrittrmatrixStatsnumDerivpillarpkgconfigplyrposteriorpurrrquadprogR6RColorBrewerRcppreshape2rlangS7scalesstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Rescorla-Wagner (Delta) Modelalt_delta
Rescorla-Wagner (Gamma) Modelalt_gamma
Rescorla-Wagner (Delta) Modelbandit2arm_delta
3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)bandit4arm_2par_lapse
4 Parameter Model, without C (choice perseveration)bandit4arm_4par
5 Parameter Model, without C (choice perseveration) but with xi (noise)bandit4arm_lapse
5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).bandit4arm_lapse_decay
4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.bandit4arm_singleA_lapse
Kalman Filterbandit4arm2_kalman_filter
3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)banditNarm_2par_lapse
4 Parameter Model, without C (choice perseveration)banditNarm_4par
Rescorla-Wagner (Delta) ModelbanditNarm_delta
Kalman FilterbanditNarm_kalman_filter
5 Parameter Model, without C (choice perseveration) but with xi (noise)banditNarm_lapse
5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).banditNarm_lapse_decay
4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.banditNarm_singleA_lapse
Exponential-Weight Mean-Variance Modelbart_ewmv
Re-parameterized version of BART model with 4 parametersbart_par4
Cumulative Modelcgt_cm
Drift Diffusion ModelchoiceRT_ddm
Drift Diffusion ModelchoiceRT_ddm_single
Exponential Subjective Value Modelcra_exp
Linear Subjective Value Modelcra_linear
Probability Weight Functiondbdm_prob_weight
Constant-Sensitivity (CS) Modeldd_cs
Constant-Sensitivity (CS) Modeldd_cs_single
Exponential Modeldd_exp
Hyperbolic Modeldd_hyperbolic
Hyperbolic Modeldd_hyperbolic_single
Function to estimate mode of MCMC samplesestimate_mode
Extract Model Comparison Estimatesextract_ic
RW + noisegng_m1
RW + noise + biasgng_m2
RW + noise + bias + pigng_m3
RW (rew/pun) + noise + bias + pigng_m4
Compute Highest-Density Intervalhdi
Hierarchical Bayesian version of the Hierarchical Gaussian Filter model for binary inputs and binary responseshgf_ibrb
Individual-level Bayesian version of the Hierarchical Gaussian Filter model for binary inputs and binary responseshgf_ibrb_single
Outcome-Representation Learning Modeligt_orl
Prospect Valence Learning (PVL) Decay-RIigt_pvl_decay
Prospect Valence Learning (PVL) Deltaigt_pvl_delta
Value-Plus-Perseveranceigt_vpp
Function to plot multiple figuresmultiplot
Other-Conferred Utility (OCU) Modelpeer_ocu
Plots the histogram of MCMC samples.plot_dist
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.plot_hdi
Plots individual posterior distributions using 'bayesplot'.plot_ind
Print model-fits (mean LOOIC or WAIC values in addition to Akaike weights) of hBayesDM Modelsprint_fit
Experience-Weighted Attraction Modelprl_ewa
Fictitious Update Modelprl_fictitious
Fictitious Update Modelprl_fictitious_multipleB
Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE)prl_fictitious_rp
Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE), without alpha (indecision point)prl_fictitious_rp_woa
Fictitious Update Model, without alpha (indecision point)prl_fictitious_woa
Reward-Punishment Modelprl_rp
Reward-Punishment Modelprl_rp_multipleB
Gain-Loss Q Learning Modelpst_gainloss_Q
Q Learning Modelpst_Q
Drift Diffusion ModelpstRT_ddm
Reinforcement Learning Drift Diffusion Model 1pstRT_rlddm1
Reinforcement Learning Drift Diffusion Model 6pstRT_rlddm6
Prospect Theory, without loss aversion (LA) parameterra_noLA
Prospect Theory, without risk aversion (RA) parameterra_noRA
Prospect Theoryra_prospect
Happiness Computational Modelrdt_happiness
Function for extracting Rhat values from an hBayesDM objectrhat
Signal detection theory modeltask2AFC_sdt
Hybrid Model, with 4 parametersts_par4
Hybrid Model, with 6 parametersts_par6
Hybrid Model, with 7 parameters (original model)ts_par7
Ideal Observer Modelug_bayes
Rescorla-Wagner (Delta) Modelug_delta
Sequential Learning Modelwcs_sql