Other packages > Find by keyword >

Landmarking  

Analysis using Landmark Models
View on CRAN: Click here


Download and install Landmarking package within the R console
Install from CRAN:
install.packages("Landmarking")

Install from Github:
library("remotes")
install_github("cran/Landmarking")

Install by package version:
library("remotes")
install_version("Landmarking", "1.0.0")



Attach the package and use:
library("Landmarking")
Maintained by
Isobel Barrott
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-15
Latest Update: 2022-02-15
Description:
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 .
How to cite:
Isobel Barrott (2022). Landmarking: Analysis using Landmark Models. R package version 1.0.0, https://cran.r-project.org/web/packages/Landmarking. Accessed 22 Dec. 2024.
Previous versions and publish date:
No previous versions
Other packages that cited Landmarking R package
View Landmarking citation profile
Other R packages that Landmarking depends, imports, suggests or enhances
Complete documentation for Landmarking
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

dmlalg  
Double Machine Learning Algorithms
Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann ...
Download / Learn more Package Citations See dependency  
composits  
Compositional, Multivariate and Univariate Time Series Outlier Ensemble
A compositional multivariate and univariate time series outlier ensemble.It uses the four R packages ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
LOGANTree  
Tree-Based Models for the Analysis of Log Files from Computer-Based Assessments
Enables researchers to model log-file data from computer-based assessments using machine-learning te ...
Download / Learn more Package Citations See dependency  
Maintainer: Qi Qin (view profile)
Rfast2  
A Collection of Efficient and Extremely Fast R Functions II
A collection of fast statistical and utility functions for data analysis. Functions for regression, ...
Download / Learn more Package Citations See dependency  
elect  
Estimation of Life Expectancies Using Multi-State Models
Functions to compute state-specific and marginal life expectancies. The computation is based on a fi ...
Download / Learn more Package Citations See dependency  

23,394

R Packages

201,798

Dependencies

63,416

Author Associations

23,395

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA