dynamichazard: Dynamic Hazard Models Using State Space Models
Dublin Core
Title
dynamichazard: Dynamic Hazard Models Using State Space Models
Subject
survival analysis, time-varying parameters, extended Kalman filter, EM algorithm,
unscented Kalman filter, parallel computing, R, Rcpp, RcppArmadillo.
unscented Kalman filter, parallel computing, R, Rcpp, RcppArmadillo.
Description
The dynamichazard package implements state space models that can provide a computationally efficient way to model time-varying parameters in survival analysis. I cover the
models and some of the estimation methods implemented in dynamichazard, apply them
to a large data set, and perform a simulation study to illustrate the methods’ computation
time and performance. One of the methods is compared with other models implemented
in R which allow for left-truncation, right-censoring, time-varying covariates, and timevarying parameters.
models and some of the estimation methods implemented in dynamichazard, apply them
to a large data set, and perform a simulation study to illustrate the methods’ computation
time and performance. One of the methods is compared with other models implemented
in R which allow for left-truncation, right-censoring, time-varying covariates, and timevarying parameters.
Creator
Benjamin Christoffersen
Source
https://www.jstatsoft.org/article/view/v099i07
Publisher
Copenhagen Business School
Date
august 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Benjamin Christoffersen, “dynamichazard: Dynamic Hazard Models Using State Space Models,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8208.