Package: Landmarking 1.0.2

Landmarking: Analysis using Landmark Models

The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see <https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.

Authors:Isobel Barrott [aut, cre], Jessica Barrett [aut], Ruth Keogh [ctb], Michael Sweeting [ctb], David Stevens [ctb]

Landmarking_1.0.2.tar.gz
Landmarking_1.0.2.zip(r-4.7)Landmarking_1.0.2.zip(r-4.6)Landmarking_1.0.2.zip(r-4.5)
Landmarking_1.0.2.tgz(r-4.6-any)Landmarking_1.0.2.tgz(r-4.5-any)
Landmarking_1.0.2.tar.gz(r-4.7-any)Landmarking_1.0.2.tar.gz(r-4.6-any)
Landmarking_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
Landmarking/json (API)
NEWS

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

Bug tracker:https://github.com/isobelbarrott/landmarking/issues

Datasets:

On CRAN:

Conda:

6.32 score 8 stars 44 scripts 252 downloads 11 exports 107 dependencies

Last updated from:04f09722a8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK182
source / vignettesOK423
linux-release-x86_64OK182
macos-release-arm64OK152
macos-oldrel-arm64OK136
windows-develOK108
windows-releaseOK111
windows-oldrelOK122
wasm-releaseOK142

Exports:add_cv_numberfind_LME_risk_setfind_LOCF_risk_setfit_LME_landmarkfit_LME_longitudinalfit_LOCF_landmarkfit_LOCF_longitudinalfit_survival_modelget_model_assessmentmixoutsampreturn_ids_with_LOCF

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercmprskcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2glmnetglobalsgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvmagrittrMASSMatrixMatrixModelsmemoisemetsmimemstatemultcompmvtnormnlmennetnumDerivparallellypecpillarpkgconfigplotrixpolsplineprodlimprogressrPublishquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenriskRegressionrlangrmarkdownrmsrpartrstudioapiS7sandwichsassscalesshapeSparseMSQUAREMstringistringrsurvivalTH.datatibbletidyselecttimeregtinytexutf8vctrsviridisLitewithrxfunyamlzoo

How to use the R package 'Landmarking'

Isobel Barrott

Rendered fromhow_to_use.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2022-11-13
Started: 2021-06-05

Introduction to Landmark Models and the R package Landmarking

Rendered fromLandmarking.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2022-11-13
Started: 2021-07-29