Offers tools for data formatting, anomaly detection, and classification
 of tree-ring data using spatial comparisons and cross-correlation. 
 Supports flexible detrending and climate–growth modeling via generalized
 additive mixed models (Wood 2017, ISBN:978-1498728331) and the 'mgcv'
 package (<https://CRAN.R-project.org/package=mgcv>), enabling robust
 analysis of non-linear trends and autocorrelated data. Provides
 standardized visual reporting, including summaries, diagnostics, and
 model performance. Compatible with '.rwl' files and tailored for the
 Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository
 (Girardin et al. (2021) <doi:10.1139/er-2020-0099>), offering
 a comprehensive and adaptable framework for dendrochronologists working
 with large and complex datasets.

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.