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censCov

Project Status: Active – The project has reached a stable, usable state and is being actively developed. minimal R version CRAN_Status_Badge packageversion Travis-CI Build Status AppVeyor Build Status Last-changedate

Linear Regression with a Randomly Censored Covariate

censCov consists of implementations of threshold regression approaches for linear regression models with a covariate subject to random censoring, including deletion threshold regression and completion threshold regression. Reverse survival regression, which flip the role of response variable and the covariate, is also considered.

Installation

You can install and load censCov from CRAN using

install.packages("censCov")
library(censCov)

You can install censCov from github with:

## install.packages("devtools")
devtools::install_github("stc04003/censCov")

Getting Started

The package vignette provides a quick demonstration for the basic usage of the main functions.

References:

Qian, J., Chiou, S.H., Maye, J.E., Atem, F., Johnson, K.A. and Betensky, R.A. (2018). Threshold regression to accommodate a censored covariate, Biometrics, 74(4): 1261--1270.

Atem, F., Qian, J., Maye J.E., Johnson, K.A. and Betensky, R.A. (2017), Linear regression with a randomly censored covariate: Application to an Alzheimer's study. Journal of the Royal Statistical Society: Series C, 66(2):313--328.

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Linear regression with a randomly censored covariate

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