Package: JMbayes2 0.5-1

JMbayes2: Extended Joint Models for Longitudinal and Time-to-Event Data

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

Authors:Dimitris Rizopoulos [aut, cre], Grigorios Papageorgiou [aut], Pedro Miranda Afonso [aut]

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NEWS

# Install 'JMbayes2' in R:
install.packages('JMbayes2', repos = c('https://drizopoulos.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/drizopoulos/jmbayes2/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • aids - Didanosine versus Zalcitabine in HIV Patients
  • aids.id - Didanosine versus Zalcitabine in HIV Patients
  • pbc2 - Mayo Clinic Primary Biliary Cirrhosis Data
  • pbc2.id - Mayo Clinic Primary Biliary Cirrhosis Data
  • prothro - Prednisone versus Placebo in Liver Cirrhosis Patients
  • prothros - Prednisone versus Placebo in Liver Cirrhosis Patients

On CRAN:

competing-riskslongitudinal-analysismixed-modelsmulti-statepersonalized-medicineprecision-medicineprediction-modelsurvival-models

43 exports 76 stars 4.11 score 36 dependencies 1 dependents 215 scripts 1.6k downloads

Last updated 2 months agofrom:82801a3180. Checks:OK: 7 NOTE: 1 WARNING: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 19 2024
R-4.5-win-x86_64OKAug 19 2024
R-4.5-linux-x86_64NOTEAug 19 2024
R-4.4-win-x86_64OKAug 19 2024
R-4.4-mac-x86_64WARNINGAug 19 2024
R-4.4-mac-aarch64OKAug 19 2024
R-4.3-win-x86_64OKAug 19 2024
R-4.3-mac-x86_64OKAug 19 2024
R-4.3-mac-aarch64OKAug 19 2024

Exports:accelerationareacalibration_metricscalibration_plotcoefcoefscompare_jmcreate_foldscrisk_setupcumuplotdensplotDexpDexpitfamilyfixefgelman_diagggdensityplotggtraceplotjmmodel.framemodel.matrixpoly2poly3poly4ranefrc_setupslopetermstraceplottvtvAUCtvBriertvEPCEtvROCvabsvaluevelocityvexpvexpitvlogvlog10vlog2vsqrt

Dependencies:clicodacolorspacefansifarverggplot2GLMMadaptivegluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmeparallellypillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalessurvivaltibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Time-Dependent Predictive Accuracy Measures for Joint Modelscalibration_metrics calibration_plot create_folds tvAUC tvAUC.jm tvAUC.tvROC tvBrier tvEPCE tvROC tvROC.jm
Didanosine versus Zalcitabine in HIV Patientsaids aids.id
Transform Competing Risks Data in Long Formatcrisk_setup
Joint Models for Longitudinal and Time-to-Event Dataacceleration area coefs Dexp Dexpit jm poly2 poly3 poly4 slope tv vabs value velocity vexp vexpit vlog vlog10 vlog2 vsqrt
Various Methods for Functions from the _coda_ Packagecoda_methods.jm cumuplot cumuplot.jm densplot densplot.jm gelman_diag gelman_diag.jm ggdensityplot ggdensityplot.jm ggtraceplot ggtraceplot.jm traceplot traceplot.jm
Various Methods for Standard Genericscoef coef.jm compare_jm family family.jm fixef fixef.jm get_links get_links.jm methods.jm model.frame model.frame.jm model.matrix model.matrix.jm ranef ranef.jm terms terms.jm
Extended Joint Models for Longitudinal and Time-to-Event DataJMbayes2-package JMbayes2
Mayo Clinic Primary Biliary Cirrhosis Datapbc2 pbc2.id
Predictions from Joint Modelsplot.predict_jm predict.jm predict.jmList
Prednisone versus Placebo in Liver Cirrhosis Patientsprothro prothros
Combine Recurring and Terminal Event Data in Long Formatrc_setup