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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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:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

bayesiancdmcmdstanrcognitive-diagnosiscognitive-diagnostic-modelsdcmdiagnostic-classification-modelspsychometricsrstanstancpp

7.60 score 13 stars 64 scripts 755 downloads 66 exports 105 dependencies

Last updated from:93a2e8745b. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK552
linux-devel-x86_64OK561
source / vignettesOK636
linux-release-arm64OK552
linux-release-x86_64OK676
macos-release-arm64OK423
macos-release-x86_64OK1050
macos-oldrel-arm64OK394
macos-oldrel-x86_64OK896
windows-develOK618
windows-releaseOK670
windows-oldrelOK646
wasm-releaseFAIL238

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