Package: JointFPM 1.3.0.9000

JointFPM: A Parametric Model for Estimating the Mean Number of Events

Implementation of a parametric joint model for modelling recurrent and competing event processes using generalised survival models as described in Entrop et al., (2025) <doi:10.1002/bimj.70038>. The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available.

Authors:Joshua P. Entrop [aut, cre, cph], Alessandro Gasparini [ctb], Mark Clements [ctb]

JointFPM_1.3.0.9000.tar.gz
JointFPM_1.3.0.9000.zip(r-4.7-any)JointFPM_1.3.0.9000.zip(r-4.6-any)JointFPM_1.3.0.9000.zip(r-4.5-any)
JointFPM_1.3.0.9000.tgz(r-4.6-any)JointFPM_1.3.0.9000.tgz(r-4.5-any)
JointFPM_1.3.0.9000.tar.gz(r-4.7-any)JointFPM_1.3.0.9000.tar.gz(r-4.6-any)
JointFPM_1.3.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
JointFPM/json (API)

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

Bug tracker:https://github.com/entjos/jointfpm/issues

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

Datasets:
  • bladder1_stacked - Stacked version of the bladder1 dataset included in the survival package

On CRAN:

Conda:

recurrent-eventssurvival-analysis

3.89 score 7 stars 11 scripts 388 downloads 3 exports 22 dependencies

Last updated from:7c417c5a7d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK156
source / vignettesOK151
linux-releaseOK153
macos-releaseOK219
macos-oldrelOK253
windows-develOK126
windows-releaseOK133
windows-oldrelOK124
wasm-releaseOK99

Exports:JointFPMmean_notest_dfs_JointFPM

Dependencies:bbmlebdsmatrixclidata.tablefastGHQuadlatticelifecyclelsodaMASSMatrixmatrixStatsmgcvmvtnormnlmenumDerivRcppRcppArmadillorlangrmutilrstpm2statmodsurvival