Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
    <arXiv:1701.06967>. In the model, a regularization term is added to the
    regression problem which approximates the counting norm of the parameters.
    By iteratively improving the approximation a sparse solution to the
    regression problem can be obtained.  In this package both the standard
    SparseStep algorithm is implemented as well as a path algorithm which uses
    golden section search to determine solutions with different values for the
    regularization parameter.

Functions for fitting, forecasting, and early detection of outbreaks in
    sparse surveillance count time series. Supports negative binomial (NB),
    self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates
    can be included in the regression component and/or the zero-modified components.
    Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with
    utilities for prediction and diagnostics.

Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.

Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

A wrapper for sparse VAR (Vector Autoregression) and
             VECM (Vector Error Correction Model) time series models estimation
             using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped
             Absolute Deviation) and MCP (Minimax Concave Penalty).
             Based on the work of Basu and Michailidis (2015)
             <doi:10.1214/15-AOS1315>.