Functions capable of performing  efficient design matrix free  penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The generic glamlasso() function solves the penalized maximum likelihood estimation (PMLE) problem in a pure generalized linear array model (GLAM) as well as in a GLAM containing a non-tensor component. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the followings 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. Furthermore this package also contains two  functions  that can be used to fit special cases of GLAMs, see glamlassoRR() and glamlassoS(). The procedure underlying these functions is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>.

Generate tables and plots to get summaries of data
person("Guillermo", "Basulto-Elias",
email = "guillermobasulto@gmail.com",
role = c("aut", "cre"))
Generate data frames for all the variables with some summaries and also some plots for numerical variables.
Several functions from the 'tidyverse' and 'GGally' packages are used.
GGally, dplyr, forcats, ggplot2, openxlsx, purrr, tibble,
tidyr, gridExtra
testthat (>= 2.1.0), covr, spelling, knitr, rmarkdown, skimr

Generalized Linear Autoregressive Moving Average Models
William T.M. Dunsmuir <w.dunsmuir@unsw.edu.au>, Cenanning Li
<cli113@aucklanduni.ac.nz>, and David J. Scott
<d.scott@auckland.ac.nz>
This package provides functions for estimation, testing and diagnostic checking 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.