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✨
DESCRIPTION |NEWS
card.svg |card.png
Landmarking/json (API)

# 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:

6.37 score 9 stars 43 scripts 444 downloads 11 exports 107 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK192
source / vignettesOK434
linux-release-x86_64OK231
macos-release-arm64OK142
macos-oldrel-arm64OK147
windows-develOK150
windows-releaseOK125
windows-oldrelOK127
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'
Introduction | Exploring the datasets | Analysis
Rendered fromhow_to_use.Rmdusingknitr::rmarkdown

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

Introduction to Landmark Models and the R package Landmarking
What is the landmark model used for? | What is the landmark model? | What are the advantages of the landmark model? | Features of the R package Landmarking | 1. Linear mixed effects modelling | 2. Competing risks modelling | 3. Cross-validation | How is the landmark model fit within the R package Landmarking?
Rendered fromLandmarking.Rmdusingknitr::rmarkdown

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