Generalized Additive and Linear Models (GLAM)
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 1
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
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- Help pages citing references
- 0%
- glam HTML
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Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.0.2Latest2024-07-09 · current release
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-07-09
- Total releases
- 1 / 2 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 36 KB
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