Package: bmm 1.0.1.9000

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 (2023) <doi:10.31234/osf.io/umt57>.

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

bmm_1.0.1.9000.tar.gz
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bmm.pdf |bmm.html
bmm/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/venpopov/bmm/issues

Datasets:

On CRAN:

5.07 score 12 stars 18 scripts 227 downloads 53 exports 79 dependencies

Last updated 6 days agofrom:7c8c33c8e2. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winNOTENov 15 2024
R-4.5-linuxNOTENov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:%>%bmfbmf2bfbmmbmm_optionsbmmformulac_bessel2sqrtexpc_sqrtexp2besselcalc_error_relative_to_nontargetscheck_dataconfigure_modelconfigure_priordefault_priordeg2raddimmdmixture2pdmixture3pdsdmfit_infofit_modelimmIMMabcIMMbscIMMfullk2sdmixture2pmixture3ppimmpmixture2ppmixture3ppostprocess_brmprint_pretty_models_mdpsdmqimmqmixture2pqmixture3pqsdmrad2degrestructurerevert_postprocess_brmrimmrmixture2prmixture3prsdmsdmsdmSimplesoftmaxsoftmaxinvstancodestandatasupported_modelsuse_model_templatewrap

Dependencies:abindbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscolorspacecpp11crayondescdigestdistributionaldplyrfansifarverfsfuturefuture.applygenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglatticelifecyclelistenvloomagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnleqslvnlmenumDerivparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsscalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Easy and Accesible Bayesian Measurement Models Using 'brms'bmm-package
Fit Bayesian Measurement Modelsbmm fit_model
View or change global bmm optionsbmm_options
Create formula for predicting parameters of a 'bmmodel'bmf bmmformula
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
Get Default priors for Measurement Models specified in BMMdefault_prior default_prior.bmmformula
Extract information from a brmsfit objectfit_info
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
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
Data from Experiment 1 reported by Oberauer & Lin (2017)oberauer_lin_2017
Restructure Old 'bmmfit' Objectsrestructure restructure.bmmfit
Signal Discrimination Model (SDM) by Oberauer (2023)sdm sdmSimple
Distribution functions for the Signal Discrimination Model (SDM)dsdm psdm qsdm rsdm SDMdist
Softmax and logsoftmax functions and their inverse functionssoftmax 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
Measurement models available in 'bmm'supported_models
Update a bmm modelupdate.bmmfit
Wrap angles that extend beyond (-pi;pi)wrap
Data from Experiment 2 reported by Zhang & Luck (2008)zhang_luck_2008