Package: landmaRk 0.1.1.9000

Victor Velasco-Pardo

landmaRk: Time-to-Event Landmark Analysis using an Array of Longitudinal and Survival Sub-Models

Provides a modular end-to-end framework for dynamic risk prediction based on time-to-event and longitudinal data. This allows flexible specifications for the longitudinal and survival sub-models. The 'landmaRk' package enables reproducible benchmarks of different model choices, including cross-validation to assess out-of-sample predictive performance. Methods are described in Velasco-Pardo, Constantine-Cooke, Lees and Vallejos (2026, manuscript under preparation) 'Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories'.

Authors:Victor Velasco-Pardo [aut, cre], Nathan Constantine-Cooke [aut], Charlie Lees [aut], Catalina Vallejos [aut]

landmaRk_0.1.1.9000.tar.gz
landmaRk_0.1.1.9000.zip(r-4.7)landmaRk_0.1.1.9000.zip(r-4.6)landmaRk_0.1.1.9000.zip(r-4.5)
landmaRk_0.1.1.9000.tgz(r-4.6-any)landmaRk_0.1.1.9000.tgz(r-4.5-any)
landmaRk_0.1.1.9000.tar.gz(r-4.7-any)landmaRk_0.1.1.9000.tar.gz(r-4.6-any)
landmaRk_0.1.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
landmaRk/json (API)
NEWS

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

Bug tracker:https://github.com/vallejosgroup/landmark/issues

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

Datasets:
  • epileptic - Dose calibration of anti-epileptic drugs data

On CRAN:

Conda:

landmarkinglongitudinal-datasurvival-analysistime-to-event

4.68 score 1 stars 10 scripts 14 exports 117 dependencies

Last updated from:50390cea31. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK242
source / vignettesOK306
linux-release-x86_64OK260
macos-release-arm64OK213
macos-oldrel-arm64OK136
windows-develOK146
windows-releaseOK170
windows-oldrelOK166
wasm-releaseOK159

Exports:check_lcmm_convergencecompute_risk_setsfit_longitudinalfit_survivalLandmarkAnalysisperformance_metricsplotpredict_longitudinalpredict_survivalpruneprune_risk_setsshowsplit_wide_dfsummary

Dependencies:backportsbase64encbootbslibcachemcheckmatecliclustercmprskcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2glmnetglobalsgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalcmmlifecyclelistenvlme4magrittrmarqLevAlgMASSMatrixMatrixModelsmemoisemetsmimeminqamultcompmvtnormnlmenloptrnnetnumDerivparallellypecpillarpkgconfigplotrixpolsplineprodlimprogressrPublishquantregR6rangerrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasriskRegressionrlangrmarkdownrmsrpartrstudioapiS7sandwichsassscalesshapespacefillrSparseMSQUAREMstringistringrsurvivalTH.datatibbletidyselecttimeregtimeROCtinytexutf8vctrsviridisLitewithrxfunyamlzoo

Cross-validation with the landmaRk package

Rendered fromlandmaRk-cv.Rmdusingknitr::rmarkdownon May 27 2026.

Last update: 2026-05-06
Started: 2025-08-15

Introduction to the landmaRk package

Rendered fromlandmaRk.Rmdusingknitr::rmarkdownon May 27 2026.

Last update: 2026-05-06
Started: 2025-06-16

Readme and manuals

Help Manual

Help pageTopics
Checks convergence of lcmm modelscheck_lcmm_convergence
Compute the list of individuals at risk at landmark timescompute_risk_sets
Compute the list of individuals at risk at landmark timescompute_risk_sets,LandmarkAnalysis-method
Dose calibration of anti-epileptic drugs dataepileptic
Fits the specified longitudinal model for time-varying covariates up to the landmark timesfit_longitudinal
Fits the specified longitudinal model for time-varying covariates up to the landmark timesfit_longitudinal,LandmarkAnalysis-method
Fits the specified survival model at the landmark times and up to the horizon times specified by the userfit_survival
Fits the specified survival model at the landmark times and up to the horizon times specified by the userfit_survival,LandmarkAnalysis-method
Creates an S4 class for a landmarking analysisLandmarkAnalysis
S4 class for performing a landmarking analysisLandmarkAnalysis-class
Performance metricsperformance_metrics
Performance metricsperformance_metrics,LandmarkAnalysis-method
Plot longitudinal observations and predicted survival curve for one individualplot,LandmarkAnalysis-method
Make predictions for time-varying covariates at specified landmark timespredict_longitudinal
Make predictions for time-varying covariates at specified landmark timespredict_longitudinal,LandmarkAnalysis-method
Make predictions for time-to-event outcomes at specified horizon timespredict_survival
Make predictions for time-to-event outcomes at specified horizon timespredict_survival,LandmarkAnalysis-method
Prunes a landmark time from a 'LandmarkAnalysis', removing the risk set, longitudinal submodel and survival submodel from the object.prune
Prune a set of individuals from a risk setprune_risk_sets
Prune a set of individuals from a risk setprune_risk_sets,LandmarkAnalysis-method
Prunes a landmark time from a 'LandmarkAnalysis', removing the risk set, longitudinal submodel and survival submodel from the object.prune,LandmarkAnalysis-method
Displays an object of class "'LandmarkAnalysis'"show,LandmarkAnalysis-method
Split a wide dataframe containing static and dynamic covariates and splits in into a dataframe with the static covariates and a list of dataframes, each associated to a dynamic covariate.split_wide_df
Summarise a LandmarkAnalysis objectsummary,LandmarkAnalysis-method