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ARCensReg  

Fitting Univariate Censored Linear Regression Model with Autoregressive Errors
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("ARCensReg", "3.0.2")



Attach the package and use:
library("ARCensReg")
Maintained by
Fernanda L. Schumacher
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-05-16
Latest Update: 2025-09-03
Description:
It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 . Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 . Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2021). Censored autoregressive regression models with Student-t innovations. arXiv preprint .
How to cite:
Fernanda L. Schumacher (2016). ARCensReg: Fitting Univariate Censored Linear Regression Model with Autoregressive Errors. R package version 3.0.2, https://cran.r-project.org/web/packages/ARCensReg. Accessed 28 Oct. 2026.
Previous versions and publish date:
1.0 (2016-05-16 14:37), 2.0 (2016-09-09 23:48), 2.1 (2016-09-11 00:54), 3.0.1 (2023-08-29 23:00), (2026-07-09 07:56)
Other packages that cited ARCensReg R package
View ARCensReg citation profile
Other R packages that ARCensReg depends, imports, suggests or enhances
Complete documentation for ARCensReg
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