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'.

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landmarkinglongitudinal-datasurvival-analysistime-to-event

4.68 score 1 stars 10 scripts

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'.

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3.00 score 10 scripts

bayefdr - Bayesian Estimation and Optimisation of Expected False Discovery Rate

Implements the Bayesian FDR control described by Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>. Allows optimisation and visualisation of expected error rates based on tail posterior probability tests. Based on code written by Catalina Vallejos for BASiCS, see Beyond comparisons of means: understanding changes in gene expression at the single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.

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2.70 score 1 scripts 261 downloads

libdr - Support Functions used by the LIBDR LCMM Analysis

This package provides support functions frequently used by Constantine et al. for their paper, "Large-scale clustering of longitudinal faecal calprotectin and C-reactive protein profiles in inflammatory bowel disease". These functions are primarily used to manipulate data and visualise findings.

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clusteringcrpdenmarkfcalibdlcmmscotland

2.23 score 17 scripts