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glam  

Generalized Additive and Linear Models (GLAM)
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("glam", "1.0.2")



Attach the package and use:
library("glam")
Maintained by
Andrew Cooper
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-07-09
Latest Update: 2024-07-09
Description:
Contains methods for fitting Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). Generalized regression models are common methods for handling data for which assuming Gaussian-distributed errors is not appropriate. For instance, if the response of interest is binary, count, or proportion data, one can instead model the expectation of the response based on an appropriate data-generating distribution. This package provides methods for fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma regression, and Binomial regression (currently GLM only) settings. Models are fit using local scoring algorithms described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.
How to cite:
Andrew Cooper (2024). glam: Generalized Additive and Linear Models (GLAM). R package version 1.0.2, https://cran.r-project.org/web/packages/glam. Accessed 12 Sep. 2026.
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