Package: bmm 1.4.1

Gidon T. Frischkorn

bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2025) <doi:10.3758/s13428-025-02643-0>.

Authors:Vencislav Popov [aut, cph], Gidon T. Frischkorn [aut, cre, cph], Chenyu Li [ctb], Paul-Christian Bürkner [cph]

bmm_1.4.1.tar.gz
bmm_1.4.1.zip(r-4.7-any)bmm_1.4.1.zip(r-4.6-any)bmm_1.4.1.zip(r-4.5-any)
bmm_1.4.1.tgz(r-4.6-any)bmm_1.4.1.tgz(r-4.5-any)
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bmm_1.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bmm/json (API)

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

Bug tracker:https://github.com/popov-lab/bmm/issues

Pkgdown/docs site:https://venpopov.com

Datasets:

On CRAN:

Conda:

5.60 score 19 stars 126 scripts 694 downloads 137 exports 85 dependencies

Last updated from:2e6761be63 (on v1.4.1). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK300
source / vignettesOK310
linux-releaseOK294
macos-releaseOK249
macos-oldrelOK223
windows-develOK306
windows-releaseOK270
windows-oldrelOK305
wasm-releaseOK285

Exports:adjust_ezdm_accuracyaggregate_sdt_cdp_dataapply_linksauc_sdtbmfbmf2bfbmmbmm_data_checkbmm_modelsbmm_optionsbmm_setupbmmformulac_bessel2sqrtexpc_sqrtexp2besselcalc_error_relative_to_nontargetscheck_dataconditional_effectsconfigure_modelconfigure_priorconstruct_m3_act_funscreate_initfuncswalddata_check_findingdata_check_findingsdcswalddddmddmdefault_priordeg2raddezdmdimmdm3dmixture2pdmixture3pdmptdsdmdsdt_cdpdsdt_dpsdtdsdt_mafcdsdt_metaddsdt_rankingdsdt_ratingdsdt_ynextract_parameter_dimensionsextract_stan_blocksezdmezdm_summary_statsfit_infofit_modelflag_contaminant_rtsimmIMMabcIMMbscIMMfullk2sdlatent_sdtm3m3_gauss_logpmixture2pmixture3pmodel_citationmptmpt_data_reshapempt_from_eqnmpt_from_stringmpt_treemrationative_parametersnative_transformparameter_infoparameterspcswaldpimmpmixture2ppmixture3ppostprocess_brmpp_checkpp_check_varspp_observablespp_simulatepppprint_model_detailsprint_pretty_models_mdprior_infopsdmqcswaldqimmqmixture2pqmixture3pqsdmrad2degrcswaldrddmrejection_samplingrestructurerevert_postprocess_brmrezdmrimmrm3rmixture2prmixture3prmptroc_observedroc_sdtrsdmrsdt_cdprsdt_dpsdtrsdt_mafcrsdt_metadrsdt_rankingrsdt_ratingrsdt_ynsdmsdmSimplesdt_biassdt_cdpsdt_cdp_logmusdt_criterionsdt_dsdt_dpsdt_logmusdt_mafcsdt_metad_logmusdt_rankingsdt_ranking_logmusdt_ratingsdt_rating_logmusdt_sensitivitysdt_thresholdssdt_ynsoftmaxsoftmaxinvstancodestandatasupported_modelsuse_model_templatevalidate_fast_guesseswrap

Dependencies:abindbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscpp11crayondescdigestdistributionaldplyrevdexpmfarverfsfuturefuture.applygenericsggdistggplot2ggridgesglobalsgluegridExtragslgtableinlineisobandlabelinglatticelifecyclelistenvloomagrittrMatrixmatrixStatsmgcvmsmmvtnormnleqslvnlmenumDerivotelparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrquadprogQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsrtdistsS7scalesStanHeadersstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Easy and Accesible Bayesian Measurement Models Using 'brms'bmm-package
Adjust Accuracy Counts for Contamination (deprecated)adjust_ezdm_accuracy
Aggregate long-format Remember/Know data for 'sdt_cdp()'aggregate_sdt_cdp_data
Apply link functions for parameters in a formula or bmmformulaapply_links
Area under the ROC curve from a fitted SDT modelauc_sdt
Fit Bayesian Measurement Modelsbmm fit_model
Human-readable pre-fit data report for bmm modelsbmm_data_check
Measurement models available in 'bmm'bmm_models
View or change global bmm optionsbmm_options
Check whether this machine can fit bmm modelsbmm_setup
Create formula for predicting parameters of a 'bmmodel'bmf bmmformula
Recognition ROC data from Broeder & Schuetz (2009, Experiment 3)broeder_schuetz_2009_e3
Convert between parametrizations of the c parameter of the SDM distributionc_bessel2sqrtexp c_parametrizations c_sqrtexp2bessel
Calculate response error relative to non-target valuescalc_error_relative_to_nontargets
Convert degrees to radians or radians to degrees.circle_transform deg2rad rad2deg
Conditional Effects for BMM Modelsconditional_effects conditional_effects.bmmfit
Get Activation Functions for different M3 versionsconstruct_m3_act_funs
Censored-Shifted Wald Modelcswald
Distribution functions for the censored shifted Wald model ('cswald')cswald_dist dcswald pcswald qcswald rcswald
Example Data from a Color Judgement Taskdata_color_judgement_task
Diffusion Decision Modelddm
Distribution function for the Diffusion Decision Model ('ddm')dddm ddm_dist rddm
Get Default priors for Measurement Models specified in BMMdefault_prior default_prior.bmmformula
emmeans support for bmmfit objectsemmeans-bmmfit emm_basis.bmmfit recover_data.bmmfit
Extract dimension from parameters in STAN parameter blockextract_parameter_dimensions
Extract code from different STAN program blocksextract_stan_blocks
EZ-Diffusion Modelezdm
Distribution functions for the EZ-Diffusion Model (ezdm)dezdm ezdm_dist rezdm
Compute Robust Summary Statistics for EZ-Diffusion Modelezdm_summary_stats
Extract information from a brmsfit objectfit_info
Flag contaminant reaction times using mixture modelingflag_contaminant_rts
Interference measurement model by Oberauer and Lin (2017).imm IMMabc IMMbsc IMMfull
Distribution functions for the Interference Measurement Model (IMM)dimm IMMdist pimm qimm rimm
Transform kappa of the von Mises distribution to the circular standard deviationk2sd
Model-implied latent decision-variable distributionslatent_sdt
The Multinomial / Memory Measurement Modelm3
Distribution functions for the Memory Measurement Model (M3)dm3 m3dist rm3
Ranking recognition data from Meyer-Grant & Jakob (2025)meyer_grant_jakob_2025
Two-parameter mixture model by Zhang and Luck (2008).mixture2p
Distribution functions for the two-parameter mixture model (mixture2p)dmixture2p mixture2p_dist pmixture2p qmixture2p rmixture2p
Three-parameter mixture model by Bays et al (2009).mixture3p
Distribution functions for the three-parameter mixture model (mixture3p)dmixture3p mixture3p_dist pmixture3p qmixture3p rmixture3p
References for a measurement modelmodel_citation
Multinomial Processing Tree (MPT) modelsmpt
Reshape wide MPT frequency tables to the long format of 'bmm()'mpt_data_reshape
Create an MPT model from an EQN model filempt_from_eqn
Create an MPT model from an MPTinR-style model stringmpt_from_string
Create a tree for a Multinomial Processing Tree (MPT) modelmpt_tree
Distribution functions for Multinomial Processing Tree (MPT) modelsdmpt mptdist rmpt
Metacognitive efficiency (M-ratio) from a fitted meta-d' SDT modelmratio
Posterior draws of model parameters on the native scalenative_parameters
Transform parameter draws from the sampling scale to the native scalenative_transform native_transform.default native_transform.non_targets
Data from Experiment 1 reported by Oberauer & Lewandowsky (2019)oberauer_lewandowsky_2019_e1
Data from Experiment 1 reported by Oberauer & Lin (2017)oberauer_lin_2017
Get parameter information for a bmm modelparameter_info parameter_info.bmmfit parameter_info.bmmodel
Plot the posterior AUC distribution from a fitted SDT modelplot.bmm_sdt_auc
Plot the latent decision-variable distributions of an SDT modelplot.bmm_sdt_latent
Plot a model-implied SDT ROC curveplot.bmm_sdt_roc
Plot the structure of MPT treesplot.mpt plot.mpt_tree
Expected response of a fitted bmm modelfitted.bmmfit posterior_epred.bmmfit
List the posterior predictive checks available for a model fitpp_check_vars
Posterior predictive check for bmmfit objectspp_check pp_check.bmmfit
Posterior predictive p-values for count modelsppp ppp.bmmfit
Priors of a fitted bmm model and where they came fromprior_info
Rejection Samplingrejection_sampling
Restructure Old 'bmmfit' Objectsrestructure restructure.bmmfit
Observed ROC points from a fitted SDT modelroc_observed
Model-implied ROC curve from a fitted SDT modelroc_sdt
Signal Discrimination Model (SDM) by Oberauer (2023)sdm sdmSimple
Distribution functions for the Signal Discrimination Model (SDM)dsdm psdm qsdm rsdm SDMdist
Continuous Dual-Process Signal Detection Theory Modelsdt_cdp
Distribution functions for Continuous Dual-Process SDT (CDP)dsdt_cdp rsdt_cdp sdt_cdp_dist
Distribution functions for dual-process SDT (DPSDT)dsdt_dpsdt rsdt_dpsdt sdt_dpsdt_dist
m-Alternative Forced Choice Signal Detection Theory Modelsdt_mafc
Distribution functions for m-AFC SDTdsdt_mafc rsdt_mafc sdt_mafc_dist
Distribution functions for meta-d' SDTdsdt_metad rsdt_metad sdt_metad_dist
Ranking Signal Detection Theory Modelsdt_ranking
Distribution functions for Ranking SDTdsdt_ranking rsdt_ranking sdt_ranking_dist
Confidence Rating Signal Detection Theory Modelsdt_rating
Distribution functions for Confidence Rating SDTdsdt_rating rsdt_rating sdt_rating_dist
Sensitivity on the noise, signal, or root-mean-square scalesdt_sensitivity
Latent decision thresholds from a fitted rating SDT modelsdt_thresholds
Yes/No Signal Detection Theory Modelsdt_yn
Distribution functions for Yes/No SDTdsdt_yn rsdt_yn sdt_yn_dist
Utility functions for Signal Detection TheorySDTdist sdt_bias sdt_criterion sdt_d
Softmax function and its inversesoftmax softmaxinv
Generate Stan code for bmm modelsstancode stancode.bmmformula
Stan data for 'bmm' modelsstandata standata.bmmformula
Create a summary of a fitted model represented by a 'bmmfit' objectsummary.bmmfit
Update a bmm modelupdate.bmmfit
Test if fast contaminants show random guessing behaviorvalidate_fast_guesses
Wrap angles that extend beyond (-pi;pi)wrap
Data from Experiment 2 reported by Zhang & Luck (2008)zhang_luck_2008