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>.

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.

Efficiently implements the Graphical Lasso algorithm,
            utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm 
            introduces an L1 penalty to derive sparse inverse covariance matrices from 
            observations of multivariate normal distributions. Features include the 
            generation of random and structured sparse covariance matrices, beneficial 
            for simulations, statistical method testing, and educational purposes in 
            graphical modeling. A unique function for regularization parameter selection 
            based on predefined sparsity levels is also offered, catering to users with
            specific sparsity requirements in their models. The methodology for sparse 
            inverse covariance estimation implemented in this package is based on the 
            work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.