Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

Create a grid-based graphviz using the following functions:
    1 - Creating the data.frame where the nodes are;
    2 - Adding and editing nodes;
    3 - Plotting these nodes.

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 (2025) <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), multivariate regression (multigaussian), smoothed support vector machines (svm1), squared support vector machines (svm2), logistic regression (binomial), proportional odds logistic regression (ordinal), 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/>.