Package: LSJM 0.1.1

LSJM: Estimate Location-Scale Joint Models

Estimation of mixed models including a subject-specific variance that can be time- and covariate-dependent or defined for within- and between-visit variability. In the joint modeling framework, the package handles left truncation, interval censoring, and multistate models, and allows a flexible dependence structure between competing events and the longitudinal marker. Estimation is performed in a frequentist framework using the Marquardt-Levenberg algorithm. Methods are described in Courcoul et al. (2025) <doi:10.1002/sim.70244> and in Courcoul et al. (2026) <doi:10.1002/bimj.70123>.

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

LSJM_0.1.1.tar.gz
LSJM_0.1.1.zip(r-4.7-x86_64)LSJM_0.1.1.zip(r-4.7-arm64)LSJM_0.1.1.zip(r-4.6-x86_64)LSJM_0.1.1.zip(r-4.6-arm64)LSJM_0.1.1.zip(r-4.5-x86_64)
LSJM_0.1.1.tgz(r-4.6-x86_64)LSJM_0.1.1.tgz(r-4.6-arm64)LSJM_0.1.1.tgz(r-4.5-x86_64)LSJM_0.1.1.tgz(r-4.5-arm64)
LSJM_0.1.1.tar.gz(r-4.7-arm64)LSJM_0.1.1.tar.gz(r-4.7-x86_64)LSJM_0.1.1.tar.gz(r-4.6-arm64)LSJM_0.1.1.tar.gz(r-4.6-x86_64)
LSJM_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LSJM/json (API)

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

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

4.16 score 1 stars 18 scripts 245 downloads 5 exports 123 dependencies

Last updated from:749a776e7e. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK530
linux-devel-x86_64OK501
source / vignettesOK355
linux-release-arm64OK550
linux-release-x86_64OK623
macos-release-arm64OK566
macos-release-x86_64OK1005
macos-oldrel-arm64OK453
macos-oldrel-x86_64OK1133
windows-devel-arm64OK753
windows-devel-x86_64OK800
windows-release-arm64OK714
windows-release-x86_64OK764
windows-oldrel-x86_64OK664
wasm-releaseOK381

Exports:dynpredlsjmlsmmranefsurvmarg

Dependencies:abindassertthatbackportsbbmlebdsmatrixbootbroomcarcarDataclicodetoolscolorspacecommonmarkcorrplotcowplotcpp11curldata.tableDerivdeSolvediagramdigestdoBydoParalleldplyrexactRankTestsfarverfastGHQuadflexsurvforeachforecastFormulafracdifffuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifggtextglobalsgluegridExtragridtextgtableisobanditeratorsjpegKernSmoothlabelinglatticelavalifecyclelistenvlitedownlme4lmtestlsodamagrittrmarkdownmarqLevAlgMASSMatrixMatrixModelsmaxstatmgcvminqamodelrmstatemuhazmvtnormnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpngpolynomprodlimprogressrpurrrquadprogquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixrstpm2S7scalesshapeSmoothHazardspacefillrSparseMSQUAREMstatmodstringistringrsurvivalsurvminertibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrxfunxml2zoo

Readme and manuals

Help Manual

Help pageTopics
Management of longitudinal datadata.manag.long
Management of data for residual partdata.manag.sigma
Management of survival datadata.manag.surv
dynpred: compute the dynamic predictions of an event.dynpred dynpred.lsjm_classicCR dynpred.lsjm_classicIDM dynpred.lsjm_classicSingle dynpred.lsjm_covDepCR dynpred.lsjm_covDepIDM dynpred.lsjm_covDepSingle dynpred.lsjm_interintraCR dynpred.lsjm_interintraIDM dynpred.lsjm_interintraSingle
Initialisation of Longitudinal Submodelinitial.long
lsjm : Estimation of a location-scale joint model for longitudinal data with flexible subject-specific variability and complex survival structureslsjm
lsmm : Estimation of a linear mixed model for longitudinal data with flexible subject-specific variability.lsmm
plot: Plot methods for 'lsjm' or 'lsmm' objectsplot.lsjm plot.lsjm_classicCR plot.lsjm_classicIDM plot.lsjm_classicSingle plot.lsjm_covDepCR plot.lsjm_covDepIDM plot.lsjm_covDepSingle plot.lsjm_interintraCR plot.lsjm_interintraIDM plot.lsjm_interintraSingle plot.lsmm_classic plot.lsmm_covDep plot.lsmm_interintra
predict: Prediction of some quantities for each subjectspredict.lsjm predict.lsjm_classicCR predict.lsjm_classicIDM predict.lsjm_classicSingle predict.lsjm_covDepCR predict.lsjm_covDepIDM predict.lsjm_covDepSingle predict.lsjm_interintraCR predict.lsjm_interintraIDM predict.lsjm_interintraSingle predict.lsmm_classic predict.lsmm_covDep predict.lsmm_interintra
ranef: the predicted random effects for all subjectsranef ranef.lsjm_classicCR ranef.lsjm_classicIDM ranef.lsjm_classicSingle ranef.lsjm_covDepCR ranef.lsjm_covDepIDM ranef.lsjm_covDepSingle ranef.lsjm_interintraCR ranef.lsjm_interintraIDM ranef.lsjm_interintraSingle ranef.lsmm_classic ranef.lsmm_covDep ranef.lsmm_interintra
The population-average probability of being free of any events at a given time $t$ and covariatessurvmarg survmarg.lsjm_classicCR survmarg.lsjm_classicIDM survmarg.lsjm_classicSingle survmarg.lsjm_covDepCR survmarg.lsjm_covDepIDM survmarg.lsjm_covDepSingle survmarg.lsjm_interintraCR survmarg.lsjm_interintraIDM survmarg.lsjm_interintraSingle
ThreeC datasetthreeC