Package: rACMEMEEV 1.0.1

rACMEMEEV: Multi-Variate Measurement Error Adjustment

A methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.

Authors:Alexander Lee [aut, cre, cph]

rACMEMEEV_1.0.1.tar.gz
rACMEMEEV_1.0.1.zip(r-4.7)rACMEMEEV_1.0.1.zip(r-4.6)rACMEMEEV_1.0.1.zip(r-4.5)
rACMEMEEV_1.0.1.tgz(r-4.6-any)rACMEMEEV_1.0.1.tgz(r-4.5-any)
rACMEMEEV_1.0.1.tar.gz(r-4.7-any)rACMEMEEV_1.0.1.tar.gz(r-4.6-any)
rACMEMEEV_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rACMEMEEV/json (API)

# Install 'rACMEMEEV' in R:
install.packages('rACMEMEEV', repos = c('https://westford14.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/westford14/racme-meev/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

5.11 score 159 downloads 14 exports 110 dependencies

Last updated from:e3b1dfeb58. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK371
source / vignettesOK368
linux-release-x86_64OK279
macos-release-arm64OK244
macos-oldrel-arm64OK251
windows-develOK235
windows-releaseOK232
windows-oldrelOK225
wasm-releaseOK153

Exports:acf_plotsacme_modelattenuation_matrixcreate_model_stringcreate_modelling_datacreate_stan_model_stringfisher_z_transformgenerate_coefficientmultivariate_modelpipelineplot_covariatessensitivity_analysisstandardize_with_returntraceplots

Dependencies:abindbackportsbayesplotBHbootbroomcallrcarcarDatacheckmateclicodacodetoolscolorspacecorrplotcowplotcpp11DerivdescdistributionaldoBydoSNOWdplyrfarverforeachforecastFormulafracdiffgenericsggplot2ggpubrggrepelggridgesggsciggsignifgluegridExtragtableinlineisobanditeratorslabelinglatticelifecyclelme4lmtestloomagrittrMASSMatrixMatrixModelsmatrixStatsmcmcMCMCpackmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivotelparallellypatchworkpbkrtestpillarpkgbuildpkgconfigplyrpolynomposteriorprocessxpspurrrquantregQuickJSRR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasreshape2rjagsrlangrstanrstatixS7scalessnowSparseMStanHeadersstringistringrsurvivaltensorAtibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Example Multivariate Adjustment Workflow
Setting up the Workflow | Generate the Tainted Dataset | Generate Validity Coefficients | Pre-Model Fitting | Attenutation Contamination Matrix | Multivariate Model | Comparing Between Unadjusted and Adjusted | A Quasi-Sensitivity Analysis | Summary | Other Resources | Traceplots and ACFs | References | Maintainers
Rendered fromexample_adjustment_workflow.Rmdusingknitr::rmarkdown

Last update: 2025-12-13
Started: 2025-09-23

JAGS vs. Stan
Setting up the Workflow. | Stan vs. JAGS Error Adjustment | Traceplots | References | Maintainers
Rendered fromjags-v-stan.Rmdusingknitr::rmarkdown

Last update: 2025-12-13
Started: 2025-09-24