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:
Landmarking_1.0.2.tar.gz
Landmarking_1.0.2.zip(r-4.5)Landmarking_1.0.2.zip(r-4.4)Landmarking_1.0.2.zip(r-4.3)
Landmarking_1.0.2.tgz(r-4.4-any)Landmarking_1.0.2.tgz(r-4.3-any)
Landmarking_1.0.2.tar.gz(r-4.5-noble)Landmarking_1.0.2.tar.gz(r-4.4-noble)
Landmarking_1.0.2.tgz(r-4.4-emscripten)Landmarking_1.0.2.tgz(r-4.3-emscripten)
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')) |
Bug tracker:https://github.com/isobelbarrott/landmarking/issues
- data_repeat_outcomes - Simulated repeat measurement and time-to-event data
Last updated 2 years agofrom:8823e08b99. Checks:OK: 5 ERROR: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win | OK | Nov 02 2024 |
R-4.5-linux | OK | Nov 02 2024 |
R-4.4-win | OK | Nov 02 2024 |
R-4.4-mac | OK | Nov 02 2024 |
R-4.3-win | ERROR | Nov 02 2024 |
R-4.3-mac | ERROR | Nov 02 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.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2022-11-13
Started: 2021-06-05
Introduction to Landmark Models and the R package Landmarking
Rendered fromLandmarking.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2022-11-13
Started: 2021-07-29