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.

Fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization.

Computes sparse vector autoregressive 				coefficients and precision matrices for time series 		chain graphical models. Fentaw Abegaz and Ernst Wit (2013)	<doi:10.1093/biostatistics/kxt005>.

A wrapper for sparse VAR/VECM 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 Sumanta Basu and George Michailidis 
             <doi:10.1214/15-AOS1315>.

Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.

Provides sparse vectors powered by ALTREP (Alternative
    Representations for R Objects) that behave like regular vectors, and
    can thus be used in data frames. Also provides tools to convert
    between sparse matrices and data frames with sparse columns and
    functions to interact with sparse vectors.

The sparse vector field consensus 
		(SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector 
		field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.