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 (2023)
    <arXiv:2209.14355> provide further details.

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.

Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.