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fbrglm  

Safe Formula-Based Regularized Generalized Linear Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("fbrglm", "0.0.1")



Attach the package and use:
library("fbrglm")
Maintained by
Koki Tsuyuzaki
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-22
Latest Update: 2026-06-22
Description:
A formula-based wrapper around 'glmnet' that brings the 'glm()'-compatible modeling workflow to regularized generalized linear models. Training-time 'terms', 'xlevels', and 'contrasts' are stored on the fit object and reused at predict time, so the design matrix is reconstructed consistently across sessions. Complete-case bookkeeping is exposed via 'nobs_info', and linearly dependent columns are detected by a QR pivot and reported as 'NA' in 'coef()' and 'summary()' (the 'stats::glm()' convention), distinguishing "not identifiable" from "shrunk to zero by the penalty". Novel factor levels at predict time raise the same error 'stats::predict.glm()' does by default, with 'on_new_levels = "na"' as a production-style opt-in. Accepts character family strings ('gaussian', 'binomial', 'poisson', 'cox', 'multinomial', 'mgaussian') and any 'glm' family object the underlying 'glmnet' itself accepts, including 'Gamma' and fixed-theta negative binomial via 'MASS::negative.binomial'.
How to cite:
Koki Tsuyuzaki (2026). fbrglm: Safe Formula-Based Regularized Generalized Linear Models. R package version 0.0.1, https://cran.r-project.org/web/packages/fbrglm. Accessed 12 Sep. 2026.
Previous versions and publish date:
(2026-07-14 10:30), 0.0.1 (2026-06-22 17:00)
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Complete documentation for fbrglm
Functions, R codes and Examples using the fbrglm R package
Full fbrglm package functions and examples
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