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]

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Landmarking.pdf |Landmarking.html
Landmarking/json (API)
NEWS

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

Peer review:

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

Datasets:

On CRAN:

11 exports 5 stars 1.26 score 108 dependencies 44 scripts 278 downloads

Last updated 2 years agofrom:8823e08b99. Checks:OK: 5 ERROR: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-winOKSep 03 2024
R-4.5-linuxOKSep 03 2024
R-4.4-winOKSep 03 2024
R-4.4-macOKSep 03 2024
R-4.3-winERRORSep 03 2024
R-4.3-macERRORSep 03 2024

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:backportsbase64encbslibcachemcheckmatecliclustercmprskcodetoolscolorspacedata.tablediagramdigestdoParalleldplyrevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2globalsgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvmagrittrMASSMatrixMatrixModelsmemoisemetsmgcvmimemstatemultcompmunsellmvtnormnlmennetnumDerivparallellypecpillarpkgconfigplotrixpolsplineprodlimprogressrPublishquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenriskRegressionrlangrmarkdownrmsrpartrstudioapisandwichsassscalesshapeSparseMSQUAREMstringistringrsurvivalTH.datatibbletidyselecttimeregtinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo

How to use the R package 'Landmarking'

Rendered fromhow_to_use.Rmdusingknitr::rmarkdownon Sep 03 2024.

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

Introduction to Landmark Models and the R package Landmarking

Rendered fromLandmarking.Rmdusingknitr::rmarkdownon Sep 03 2024.

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