Package: gmvjoint 0.4.5

James Murray

gmvjoint: Joint Models of Survival and Multivariate Longitudinal Data

Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

Authors:James Murray [aut, cre]

gmvjoint_0.4.5.tar.gz
gmvjoint_0.4.5.tar.gz(r-4.7-arm64)gmvjoint_0.4.5.tar.gz(r-4.7-x86_64)gmvjoint_0.4.5.tar.gz(r-4.6-arm64)gmvjoint_0.4.5.tar.gz(r-4.6-x86_64)
gmvjoint_0.4.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
gmvjoint/json (API)
NEWS

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

Bug tracker:https://github.com/jamesmurray7/gmvjoint/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • PBC - Primary biliary cirrhosis data

On CRAN:

Conda:

openblascppopenmp

2.00 score 20 scripts 196 downloads 9 exports 69 dependencies

Last updated from:e44282881b. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK179
linux-devel-x86_64OK184
source / vignettesOK223
linux-release-arm64OK190
linux-release-x86_64OK204
wasm-releaseOK145

Exports:boot.jointcond.ranefsdynPredjointparseCoxphrgenpoisROCsimDataxtable.joint

Dependencies:backportsbootbroomclicolorspacecowplotcpp11DerivdoBydplyrfarverforecastfracdiffgenericsggplot2glmmTMBgluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixmgcvmicrobenchmarkminqamodelrmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpracmapurrrR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7sandwichscalesstatmodstringistringrsurvivaltibbletidyrtidyselecttimeDateTMBurcautf8vctrsviridisLitewithrxtablezoo