Fits user-specified (GLM-) models with group lasso penalty.

Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2024) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), logistic regression (binomial), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.

Efficient algorithms for fitting the regularization path of linear
  regression, GLM, and Cox regression models with grouped penalties.  This
  includes group selection methods such as group lasso, group MCP, and
  group SCAD as well as bi-level selection methods such as the group
  exponential lasso, the composite MCP, and the group bridge.  For more
  information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>,
  Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang
  (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015)
  <doi:10.1111/biom.12300>, or visit the package homepage
  <https://pbreheny.github.io/grpreg/>.

Provides tools for sparse regression modelling with grouped predictors using the group 
    subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver 
    near optimal estimates. The group subset penalty can be combined with a group lasso or ridge 
    penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping 
    groups.

Design of group sequential trials, including non-binding futility analysis
  at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).