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invGauss  

Threshold Regression that Fits the (Randomized Drift) Inverse Gaussian Distribution to Survival Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("invGauss", "1.2")



Attach the package and use:
library("invGauss")
Maintained by
Hakon K. Gjessing
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-03-28
Latest Update: 2022-05-20
Description:
Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
How to cite:
Hakon K. Gjessing (2014). invGauss: Threshold Regression that Fits the (Randomized Drift) Inverse Gaussian Distribution to Survival Data. R package version 1.2, https://cran.r-project.org/web/packages/invGauss. Accessed 04 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:50), 1.1 (2014-03-28 14:06)
Other packages that cited invGauss R package
View invGauss citation profile
Other R packages that invGauss depends, imports, suggests or enhances
Complete documentation for invGauss
Functions, R codes and Examples using the invGauss R package
Some associated functions: d.oropha.rec . invGauss . summary.invGauss . 
Some associated R codes: f.B.R . f.B.neglog.deriv.R . f.fb.R . f.fb.neglog.deriv.R . f.grovest3.R . invGauss.R . predict.invGauss.R . print.invGauss.R . print.summary.invGauss.R . summary.invGauss.R .  Full invGauss package functions and examples
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