Package: FlexVarJM 0.1.0

Léonie Courcoul

FlexVarJM: Estimate Joint Models with Subject-Specific Variance

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arxiv:2306.16785>).

Authors:Léonie Courcoul [aut, cre], Antoine Barbieri [aut], Hélène Jacqmin-Gadda [aut]

FlexVarJM_0.1.0.tar.gz
FlexVarJM_0.1.0.zip(r-4.7)FlexVarJM_0.1.0.zip(r-4.6)FlexVarJM_0.1.0.zip(r-4.5)
FlexVarJM_0.1.0.tgz(r-4.6-x86_64)FlexVarJM_0.1.0.tgz(r-4.6-arm64)FlexVarJM_0.1.0.tgz(r-4.5-x86_64)FlexVarJM_0.1.0.tgz(r-4.5-arm64)
FlexVarJM_0.1.0.tar.gz(r-4.7-arm64)FlexVarJM_0.1.0.tar.gz(r-4.7-x86_64)FlexVarJM_0.1.0.tar.gz(r-4.6-arm64)FlexVarJM_0.1.0.tar.gz(r-4.6-x86_64)
FlexVarJM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
FlexVarJM/json (API)

# Install 'FlexVarJM' in R:

install.packages('FlexVarJM', repos = c('https://leoniecourcoul.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/leoniecourcoul/flexvarjm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

Data_toy - Data_toy

On CRAN:

openblas cpp

3.70 score 1 stars 1 scripts 162 downloads 4 exports 100 dependencies

Last updated from:599c1addcd. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	218
linux-devel-x86_64	NOTE	212
source / vignettes	OK	279
linux-release-arm64	NOTE	227
linux-release-x86_64	NOTE	232
macos-release-arm64	NOTE	186
macos-release-x86_64	NOTE	400
macos-oldrel-arm64	NOTE	238
macos-oldrel-x86_64	NOTE	402
windows-devel	NOTE	332
windows-release	NOTE	141
windows-oldrel	NOTE	162
wasm-release	OK	156

Exports:goodness_of_fit lsjm lsmm predyn

Dependencies:abind backports boot broom car carData cli codetools colorspace commonmark corrplot cowplot cpp11 curl Deriv doBy doParallel dplyr exactRankTests farver foreach forecast Formula fracdiff generics ggplot2 ggpubr ggrepel ggsci ggsignif ggtext glue gridExtra gridtext gtable isoband iterators jpeg labeling lattice lcmm lifecycle litedown lme4 lmtest magrittr markdown marqLevAlg MASS Matrix MatrixModels maxstat mgcv microbenchmark minqa modelr mvtnorm nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig png polynom purrr quantreg R6 randtoolbox rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rngWELL rstatix S7 scales spacefillr SparseM stringi stringr survival survminer tibble tidyr tidyselect timeDate urca utf8 vctrs viridisLite withr xfun xml2 zoo

FlexVarJM

Rendered fromFlexVarJM.Rmdusingknitr::rmarkdown

Last update: 2023-06-07
Started: 2023-06-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Data_toy	Data_toy
Initialisation of Survival Data at Gauss Kronrod time points	data.GaussKronrod
Initialisation of Survival Data at Gauss Kronrod time points 2	data.GaussKronrod2
Management of longitudinal data	data.manag.long
Management of survival data	data.manag.surv
Management of data for longitudinal submodel	data.time
Gauss-Kronrod nodes and weights	gaussKronrod
Predictions for the goodness of fit, of the random effects, the current value for each individuals and the cumulative hazard function for both events	goodness_of_fit
Initialisation of Longitudinal Submodel	initial.long
Log-likelihood computation	log_llh
Log-likelihood computation in RCPP	log_llh_rcpp
Log-likelihood computation in RCPP with variance-covariance matrix for the random effects	log_llh_rcpp_cov
lsjm : Estimation of joint model for longitudinal data with a subject-specific time-dependent variability and time-to-event data.	lsjm
lsmm : Estimation of location scale mixed model	lsmm
Predictions computation	pred_s.t.bootstrap.tps
Predictions computation	pred_s.t.ponctuel.tps
Dynamic prediction for new individuals	predyn