glam: Generalized Additive and Linear Models (GLAM)
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>.
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