Kernel regularized least squares, also known as kernel ridge regression, 
    is a flexible machine learning method. This package implements this method by 
    providing a smooth term for use with 'mgcv' and uses random sketching to 
    facilitate scalable estimation on large datasets. It provides additional 
    functions for calculating marginal effects after estimation and for use with 
    ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), 
    and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024)
    <doi:10.1017/pan.2023.27> provide further details.

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>.

Efficient design matrix free  lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a  component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs.