Package: TreeBUGS 1.6.0

TreeBUGS: Hierarchical Multinomial Processing Tree Modeling

User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <doi:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <doi:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <doi:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) <doi:10.1037/met0000561>.

Authors:Daniel W. Heck [aut, cre], Nina R. Arnold [aut, dtc], Denis Arnold [aut], Alexander Ly [ctb], Marius Barth [ctb]

TreeBUGS_1.6.0.tar.gz
TreeBUGS_1.6.0.zip(r-4.7)TreeBUGS_1.6.0.zip(r-4.6)TreeBUGS_1.6.0.zip(r-4.5)
TreeBUGS_1.6.0.tgz(r-4.6-x86_64)TreeBUGS_1.6.0.tgz(r-4.6-arm64)TreeBUGS_1.6.0.tgz(r-4.5-x86_64)TreeBUGS_1.6.0.tgz(r-4.5-arm64)
TreeBUGS_1.6.0.tar.gz(r-4.7-arm64)TreeBUGS_1.6.0.tar.gz(r-4.7-x86_64)TreeBUGS_1.6.0.tar.gz(r-4.6-arm64)TreeBUGS_1.6.0.tar.gz(r-4.6-x86_64)
TreeBUGS_1.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TreeBUGS/json (API)

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

Bug tracker:https://github.com/danheck/treebugs/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
Datasets:
  • arnold2013 - Data of a Source-Monitoring Experiment

On CRAN:

Conda:

openblascppjags

8.00 score 13 stars 107 scripts 526 downloads 3 mentions 34 exports 12 dependencies

Last updated from:8601a71e6d. Checks:13 OK. Indexed: yes.

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source / vignettesOK223
linux-release-arm64OK176
linux-release-x86_64OK153
macos-release-arm64OK92
macos-release-x86_64OK228
macos-oldrel-arm64OK125
macos-oldrel-x86_64OK239
windows-develOK172
windows-releaseOK161
windows-oldrelOK144
wasm-releaseOK168

Exports:BayesFactorMPTBayesFactorSlopebetaMPTbetaMPTcppbetweenSubjectMPTcorrelationPosteriorextendMPTgenBetaMPTgenMPTgenTraitMPTgetGroupMeansgetParamgetSamplesmarginalMPTplotDistributionplotFitplotFreqplotParamplotPriorplotPriorPostposteriorPredictivePPPpriorPredictiveprobitInversereadEQNsimpleMPTsummarizeMCMCsummarizeMPTtestHetChitestHetPermtraitMPTtransformedParametersWAICwithinSubjectEQN

Dependencies:codacontfracdeSolveelliptichypergeolatticelogsplineMASSRcppRcppArmadillorjagsrunjags

TreeBUGS: Advanced MPT Modeling
Advanced MPT Modeling | A) Including Continuous Covariates | B) Including Discrete Factors in a Latent-Trait MPT Model | C) Sampling Transformed Parameters | D) Generate Simulated Data Sets
Rendered fromTreeBUGS_2_extended.Rmdusingknitr::rmarkdown

Last update: 2023-02-23
Started: 2016-07-13

TreeBUGS: Introduction to Hierarchical MPT Modeling
General Procedure of Using TreeBUGS | 1. Step: MPT model file in EQN syntax | 2. Step: Data Set with Individual Frequencies | 3. Step: Fit Hierarchical MPT Model | 4. Step: Check convergence of MCMC chains | 5. Step: Summarize and Plot Results | References
Rendered fromTreeBUGS_1_intro.Rmdusingknitr::rmarkdown

Last update: 2023-02-23
Started: 2016-07-13

Heck, Arnold, & Arnold (2018): TreeBUGS paper (Behavior Research Methods)
TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling
Rendered fromHeck_2018_BRM.pdf.asisusingR.rsp::asis

Last update: 2018-08-07
Started: 2018-08-07

Readme and manuals

Help Manual

Help pageTopics
TreeBUGS: Hierarchical Multinomial Processing Tree ModelingTreeBUGS-package TreeBUGS
Data of a Source-Monitoring Experimentarnold2013
Bayes Factors for Simple (Nonhierarchical) MPT ModelsBayesFactorMPT
Bayes Factor for Slope Parameters in Latent-Trait MPTBayesFactorSlope
Fit a Hierarchical Beta-MPT ModelbetaMPT
C++ Sampler for Hierarchical Beta-MPT ModelbetaMPTcpp
Between-Subject Comparison of ParametersbetweenSubjectMPT
Posterior Distribution for CorrelationscorrelationPosterior
Extend MCMC Sampling for MPT ModelextendMPT
Generate Data for Beta MPT ModelsgenBetaMPT
Generate MPT FrequenciesgenMPT
Generate Data for Latent-Trait MPT ModelsgenTraitMPT
Get Mean Parameters per GroupgetGroupMeans
Get Parameter Posterior StatisticsgetParam
Get Posterior Samples from Fitted MPT ModelgetSamples
Marginal Likelihood for Simple MPTmarginalMPT
Plot Convergence for Hierarchical MPT Modelsplot.betaMPT plot.simpleMPT plot.traitMPT
Plot Distribution of Individual EstimatesplotDistribution
Plot Posterior Predictive Mean FrequenciesplotFit
Plot Raw FrequenciesplotFreq
Plot Parameter EstimatesplotParam
Plot Prior DistributionsplotPrior
Plot Prior vs. Posterior DistributionplotPriorPost
Get Posterior Predictive SamplesposteriorPredictive
Compute Posterior Predictive P-ValuesPPP
Prior Predictive SamplespriorPredictive
Probit-Inverse of Group-Level Normal DistributionprobitInverse
Read multiTree filesreadEQN
C++ Sampler for Standard (Nonhierarchical) MPT ModelssimpleMPT
MCMC SummarysummarizeMCMC
Summarize JAGS Output for Hierarchical MPT ModelssummarizeMPT
Chi-Square Test of HeterogeneitytestHetChi
Permutation Test of HeterogeneitytestHetPerm
Fit a Hierarchical Latent-Trait MPT ModeltraitMPT
Get Transformed ParameterstransformedParameters
WAIC: Widely Applicable Information Criterion-.waic print.waic print.waic_difference WAIC
Generate EQN Files for Within-Subject DesignswithinSubjectEQN