Other packages > Find by keyword >

calibmsm  

Calibration Plots for the Transition Probabilities from Multistate Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("calibmsm", "1.1.3")



Attach the package and use:
library("calibmsm")
Maintained by
Alexander Pate
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-11-30
Latest Update: 2025-06-13
Description:
Assess the calibration of an existing (i.e. previously developed) multistate model through calibration plots. Calibration is assessed using one of three methods. 1) Calibration methods for binary logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 2) Calibration methods for multinomial logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 3) Pseudo-values estimated using the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction with landmarking when required. These calibration plots evaluate the calibration (in a validation cohort of interest) of the transition probabilities estimated from an existing multistate model. While package development has focused on multistate models, calibration plots can be produced for any model which utilises information post baseline to update predictions (e.g. dynamic models); competing risks models; or standard single outcome survival models, where predictions can be made at any landmark time. The underpinning methodology is currently undergoing peer review; see Pate et al. (2023) and Pate et al. (2023) .
How to cite:
Alexander Pate (2023). calibmsm: Calibration Plots for the Transition Probabilities from Multistate Models. R package version 1.1.3, https://cran.r-project.org/web/packages/calibmsm. Accessed 15 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:23), 1.0.0 (2023-11-30 20:50), 1.1.0 (2024-05-13 13:33), 1.1.1 (2024-06-14 11:40), 1.1.2 (2025-06-06 13:10)
Other packages that cited calibmsm R package
View calibmsm citation profile
Other R packages that calibmsm depends, imports, suggests or enhances
Complete documentation for calibmsm
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

pulseTD  
Identification of Transcriptional Dynamics using Pulse Models via 4su-Seq Data and RNA-Seq Data
A tool for analyzing the transcription, processing and degradation rates of genes by 4sU-seq (the Me ...
Download / Learn more Package Citations See dependency  
binhf  
Haar-Fisz Functions for Binomial Data
Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009). ...
Download / Learn more Package Citations See dependency  
PermAlgo  
Permutational Algorithm to Simulate Survival Data
This version of the permutational algorithm generates a dataset in which event and censoring times ...
Download / Learn more Package Citations See dependency  
SECFISH  
Disaggregate Variable Costs
These functions were developed within SECFISH project (Strengthening regional cooperation in the are ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
gamlss.add  
Extra Additive Terms for Generalized Additive Models for Location Scale and Shape
Interface for extra smooth functions including tensor products, neural networks and decision trees. ...
Download / Learn more Package Citations See dependency  

27,806

R Packages

239,283

Dependencies

73,837

Author Associations

27,807

Publication Badges

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