Package: measr 2.0.1.9000

W. Jake Thompson

measr: Bayesian Psychometric Measurement Using 'Stan'

Estimate diagnostic classification models (also called cognitive diagnostic models) with 'Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional model constraints. Using the generated 'Stan' code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics.

Authors:W. Jake Thompson [aut, cre], Jeffrey Hoover [aut], Auburn Jimenez [ctb], Nathan Jones [ctb], Matthew Johnson [cph], University of Kansas [cph], Institute of Education Sciences [fnd]

measr_2.0.1.9000.tar.gz
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measr_2.0.1.9000.tgz(r-4.6-x86_64)measr_2.0.1.9000.tgz(r-4.6-arm64)measr_2.0.1.9000.tgz(r-4.5-x86_64)measr_2.0.1.9000.tgz(r-4.5-arm64)
measr_2.0.1.9000.tar.gz(r-4.7-arm64)measr_2.0.1.9000.tar.gz(r-4.7-x86_64)measr_2.0.1.9000.tar.gz(r-4.6-arm64)measr_2.0.1.9000.tar.gz(r-4.6-x86_64)
measr_2.0.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
measr/json (API)

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

Bug tracker:https://github.com/r-dcm/measr/issues

Pkgdown/docs site:https://measr.r-dcm.org

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

bayesiancdmcmdstanrcognitive-diagnosiscognitive-diagnostic-modelsdcmdiagnostic-classification-modelspsychometricsrstanstanonetbbcpp

7.58 score 13 stars 66 scripts 536 downloads 66 exports 105 dependencies

Last updated from:368e2c01f7. Checks:13 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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linux-devel-x86_64WARNING514
source / vignettesOK848
linux-release-arm64WARNING595
linux-release-x86_64WARNING686
macos-release-arm64WARNING575
macos-release-x86_64WARNING939
macos-oldrel-arm64WARNING552
macos-oldrel-x86_64WARNING1059
windows-devel-arm64WARNING635
windows-devel-x86_64WARNING784
windows-release-arm64WARNING616
windows-release-x86_64WARNING671
windows-oldrel-x86_64WARNING690
wasm-releaseOK312

Exports::=.dataadd_criterionadd_fitadd_reliabilityadd_respondent_estimatesaicas_drawsas_labelas_namebayes_factorbayesnetbiccdicmdstanrcreate_profilescrumdcm_estimatedcm_specifydefault_dcm_priorsdinadinoEenquoenquosfit_m2fit_ppmcget_parametersgqshdcmindependentlcdmlog_mllloglik_arrayloglinearlooloo_comparemcmcmeasr_dcmmeasr_examplesmeasr_extractmeasrdcmncrumnidanidooptimpathfinderPrpriorqmatrix_validationreliabilityrstanrvar_madrvar_maxrvar_meanrvar_medianrvar_minrvar_prodrvar_sdrvar_sumrvar_varscoreunconstrainedvariationalwaicyens_q3

Dependencies:abindbackportsbase64encBHbitbit64bootbridgesamplingBrobdingnagbroomcachemcallrcheckmateclicliprcodacpp11crayoncurldagittydata.tabledcm2dcmstandescdistributionaldplyrdtplyrfarverfastmapforcatsfsgenericsggdagggforceggplot2ggraphggrepelglueGPArotationgraphlayoutsgridExtragtablehmsigraphinlineisobandjsonlitelabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisemnormtmodelrmvtnormnlmenumDerivotelpillarpkgbuildpkgconfigpolyclipposteriorprettyunitsprocessxprogresspspsychpurrrQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrdcmchecksreadrrlangrstanrstantoolsS7scalesStanHeadersstringistringrsystemfontstensorAtibbletidygraphtidyrtidyselecttweenrtzdbutf8V8vctrsviridisviridisLitevroomwithr

Getting started with measr
Installation | rstan | cmdstanr | measr | Model Estimation | Model Evaluation | References
Rendered frommeasr.Rmdusingknitr::rmarkdown

Last update: 2026-01-11
Started: 2023-03-23

measr: Bayesian psychometric measurement using Stan
Summary | Statement of need | Acknowledgments | References
Rendered frompaper.Rmdusingknitr::rmarkdown

Last update: 2026-01-10
Started: 2023-11-06