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'.

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>.

Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.

Contains functions for a flexible varying-coefficient landmark model by incorporating multiple short-term events into the prediction of long-term survival probability. For more information about landmark prediction please see Li, W., Ning, J., Zhang, J., Li, Z., Savitz, S.I., Tahanan, A., Rahbar.M.H., (2023+). "Enhancing Long-term Survival Prediction with Multiple Short-term Events: Landmarking with A Flexible Varying Coefficient Model".

Nonparametric methods for landmark prediction of long-term survival outcomes, incorporating covariate and short-term event information. The package supports the construction of flexible varying-coefficient models that use discrete covariates, as well as multiple continuous covariates.
 The goal is to improve prediction accuracy when censored short-term events are available as predictors, using robust nonparametric procedures that do not require correct model specification and avoid restrictive parametric assumptions found in alternative methods.
 More information on these methods can be found in Parast et al. 2012 <doi:10.1080/01621459.2012.721281>, 
 Parast et al. 2011 <doi:10.1002/bimj.201000150>, and Parast and Cai 2013 <doi:10.1002/sim.5776>.
 A tutorial for this package is available here: <https://www.laylaparast.com/landpred>.