Package: JointAI 1.1.0

JointAI: Joint Analysis and Imputation of Incomplete Data

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.

Authors:Nicole S. Erler [aut, cre]

JointAI_1.1.0.tar.gz
JointAI_1.1.0.zip(r-4.7)JointAI_1.1.0.zip(r-4.6)JointAI_1.1.0.zip(r-4.5)
JointAI_1.1.0.tgz(r-4.6-any)JointAI_1.1.0.tgz(r-4.5-any)
JointAI_1.1.0.tar.gz(r-4.7-any)JointAI_1.1.0.tar.gz(r-4.6-any)
JointAI_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
JointAI/json (API)
NEWS

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

Bug tracker:https://github.com/nerler/jointai/issues

Pkgdown/docs site:https://nerler.github.io

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:
  • longDF - Longitudinal example dataset
  • NHANES - National Health and Nutrition Examination Survey (NHANES) Data
  • PBC - PBC data
  • simLong - Simulated Longitudinal Data in Long and Wide Format
  • simWide - Simulated Longitudinal Data in Long and Wide Format
  • wideDF - Cross-sectional example dataset

On CRAN:

Conda:

bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp

8.19 score 29 stars 1 packages 64 scripts 670 downloads 2 mentions 43 exports 41 dependencies

Last updated from:ab46e5a5cd. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK192
source / vignettesOK243
linux-release-x86_64OK200
macos-release-arm64OK154
macos-oldrel-arm64OK223
windows-develOK171
windows-releaseOK150
windows-oldrelOK145
wasm-releaseOK138

Exports:add_samplesauto_corrauto_corr_plotbetamm_impbetareg_impbsclean_survnameclm_impclmm_impcoxph_impcross_corrcross_corr_plotdefault_hyperparsdensplotextract_stateget_MIdatget_missinfoglm_impglme_impglmer_impGR_critJM_implist_modelslm_implme_implmer_implognorm_implognormmm_impMC_errormd_patternmlogit_impmlogitmm_impnsparametersplot_allplot_imp_distrpredDFrd_vcovset_refcatsum_durationSurvsurvreg_imptraceplot

Dependencies:briocallrclicodacodetoolscrayondescdiffobjdigestellipseevaluatefftwtoolsfsfutureglobalsgluejsonlitelatticelifecyclelistenvmagrittrMASSmathjaxrMatrixmcmcseparallellypkgbuildpkgloadpraiseprocessxpsR6RcppRcppArmadillorjagsrlangrprojrootsurvivaltestthatwaldowithr

After Fitting

Rendered fromAfterFitting.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2026-02-22
Started: 2018-12-03

MCMC Settings

Rendered fromMCMCsettings.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2026-01-05
Started: 2018-12-03

Model Specification

Rendered fromModelSpecification.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2026-02-22
Started: 2018-08-10

Parameter Selection

Rendered fromSelectingParameters.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2026-02-22
Started: 2018-08-02

Readme and manuals

Help Manual

Help pageTopics
Continue sampling from an object of class JointAIadd_samples
Autocorrelation of MCMC samplesauto_corr auto_corr_plot
Convert a survival outcome to a model nameclean_survname
Cross-correlation of MCMC samplescross_corr cross_corr_plot
Get the default values for hyper-parametersdefault_hyperpars
Plot the posterior density from object of class JointAIdensplot densplot.JointAI
Return the current state of a 'JointAI' modelextract_state
Extract multiple imputed datasets from an object of class JointAIget_MIdat
Obtain a summary of the missing values involved in an object of class JointAIget_missinfo
Gelman-Rubin criterion for convergenceGR_crit
Convert a survival outcome to a model nameinternal_clean_survname
JointAI: Joint Analysis and Imputation of Incomplete DataJointAI-package JointAI
Fitted object of class 'JointAI'JointAIObject
List model detailslist_models
Longitudinal example datasetlongDF
Calculate and plot the Monte Carlo errorMC_error plot.MCElist
Missing data patternmd_pattern
Joint Analysis and Imputation of incomplete databetamm_imp betareg_imp clmm_imp clm_imp coxph_imp glmer_imp glme_imp glm_imp JM_imp lmer_imp lme_imp lm_imp lognormmm_imp lognorm_imp mlogitmm_imp mlogit_imp model_imp survreg_imp
National Health and Nutrition Examination Survey (NHANES) DataNHANES
Parameter names of an JointAI objectparameters
PBC dataPBC
Visualize the distribution of all variables in the datasetplot_all
Plot the distribution of observed and imputed valuesplot_imp_distr
Plot an object object inheriting from class 'JointAI'plot.JointAI
Predict values from an object of class JointAIpredict.JointAI
Summarize the results from an object of class JointAIcoef.JointAI confint.JointAI print.Dmat print.JointAI print.summary.JointAI summary.JointAI
Extract the random effects variance covariance matrixrd_vcov
Extract residuals from an object of class JointAIresiduals.JointAI
Specify reference categories for all categorical covariates in the modelset_refcat
Parameters used by several functions in JointAIsharedParams
Simulated Longitudinal Data in Long and Wide FormatsimLong simWide
Calculate the sum of the computational duration of a JointAI objectsum_duration
Create traceplots for a MCMC sampletraceplot traceplot.JointAI
Cross-sectional example datasetwideDF