Package: missingHE 1.6.2

Andrea Gabrio

missingHE: Missing Cost Effectiveness Models

Performs Bayesian cost-effectiveness analysis with missing outcome data. Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. The package allows to assess model convergence and summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.

Authors:Andrea Gabrio [aut, cre, cph]

missingHE_1.6.2.tar.gz
missingHE_1.6.2.zip(r-4.7-any)missingHE_1.6.2.zip(r-4.6-any)
missingHE_1.6.2.tar.gz(r-4.7-any)missingHE_1.6.2.tar.gz(r-4.6-any)
missingHE_1.6.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
missingHE/json (API)

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

Bug tracker:https://github.com/angabrio/missinghe/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • MenSS - MenSS economic data on STIs
  • PBS - PBS economic data on intellectual disability and challenging behaviour

On CRAN:

Conda:

jagscpp

3.68 score 1 stars 40 scripts 466 downloads 13 exports 149 dependencies

Last updated from:35b5effb5d. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK187
source / vignettesOK467
linux-releaseOK231
windows-develOK131
windows-releaseOK125
wasm-releaseOK169

Exports:data_read_hurdledata_read_lmdmdata_read_patterndata_read_selectiondiagnostichurdlejagsresultslmdmpatternpicpostsumppcselection

Dependencies:abindaskpassbackportsbase64encbayesplotBCEAbootbroombslibcachemcarcarDatacheckmatechkclicodacolorspacecorrplotcowplotcpp11crayoncrosstalkcurldata.tabledbartsDerivdigestdistributionaldoBydplyrearthevaluateextrasfarverfastmapfontawesomeforcatsforecastFormulafracdifffsgenericsGGallyggdistggmcmcggplot2ggpubrggrepelggridgesggsciggsignifggstatsggthemesgluegridExtragtablehighrhmshtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4lmtestloomagrittrMASSMatrixMatrixModelsmatrixStatsmcmcrmemoisemgcvmimeminqamodelrmvtnormnlistnlmenloptrnnetnumDerivopensslotelpatchworkpbkrtestpillarpkgconfigplotlyplotmoplotrixplyrpolynomposteriorprettyunitsprogresspromisespurrrquadprogquantregR2jagsR2WinBUGSR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rjagsrlangrmarkdownrstatixS7sassscalesSparseMstringistringrsurvivalsystensorAtermtibbletidyrtidyselecttimeDatetinytexuniversalsurcautf8vctrsviridisLitevoiwithrxfunyamlzoo

Fitting MNAR models in missingHE
Handling MNAR using selection models | Handling MNAR using pattern mixture models | Handling MNAR using hurdle models
Rendered fromFitting_MNAR_models_in_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2020-06-25

Introduction to missingHE
Modelling Framework | Missingness Approach | Selection Models | Pattern Mixture Models | Hurdle Models | The MenSS trial | Model fitting | Fitting models under MAR | Model assessment | Convergence diagnostics | Posterior predictive checks | Predictive information criteria | Results inspection | Summarise parameter estimates | Inspect imputed values | Summarise health economic results | Probabilistic sensitivity analysis
Rendered fromIntroduction_to_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2020-06-25

Longitudinal models in missingHE
Modelling Framework | Missingness Approach | The PBS trial | Model fitting | Fitting models under MAR | Model assessment | Convergence diagnostics | Posterior predictive checks | Predictive information criteria | Results inspection | Summarise parameter estimates | Inspect imputed values | Summarise health economic results | Probabilistic sensitivity analysis
Rendered fromLongitudinal_models_in_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2023-03-21

Model and output customisation in missingHE
Modelling assumptions | Outcome distribution | Missingness mechanism | Outcome model structure | Prior distributions | Conclusions
Rendered fromModel_customisation_in_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-10-13
Started: 2020-06-25