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sglg  

Fitting Semi-Parametric Generalized log-Gamma Regression Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("sglg", "0.2.7")



Attach the package and use:
library("sglg")
Maintained by
Carlos Alberto Cardozo Delgado
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-09-22
Latest Update: 2022-09-04
Description:
Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right-censoring. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression or semi-parametric functions, whose non-parametric components may be approximated by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes the generalized extreme value and standard normal distributions as important special cases. Inference is based on penalized likelihood and bootstrap methods. Also, some numerical and graphical devices for diagnostic of the fitted models are offered.
How to cite:
Carlos Alberto Cardozo Delgado (2017). sglg: Fitting Semi-Parametric Generalized log-Gamma Regression Models. R package version 0.2.7, https://cran.r-project.org/web/packages/sglg. Accessed 07 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:03), 0.1.0 (2017-09-22 10:36), 0.1.1 (2017-11-13 18:40), 0.1.2 (2017-12-05 22:55), 0.1.3 (2018-04-15 16:10), 0.1.4 (2019-02-20 21:40), 0.1.5 (2019-07-19 06:30), 0.1.6 (2020-04-25 16:40), 0.1.7 (2020-09-19 15:30), 0.1.8 (2020-12-01 01:30), 0.1.9 (2021-01-24 17:50), 0.1.10 (2021-09-18 06:30), 0.2.0 (2021-10-11 15:20), 0.2.1 (2022-02-21 16:20), 0.2.2 (2022-09-04 05:50), 0.2.3 (2025-11-27 21:00), 0.2.4 (2025-12-09 19:10), 0.2.5 (2025-12-19 02:10), 0.2.6 (2026-01-14 21:50)
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