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 (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020).

Fits sparse linear varying coefficient models (VCMs), 
    which assert a linear relationship between an outcome and several covariates 
    that is allowed to change as functions of additional variables known as effect modifiers. 
    Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) 
    is comparable to or larger than the number of observations. 
    Approximates the coefficient functions using a version of Bayesian Additive Regression Trees 
    that can perform global-local shrinkage. 
    For more details see Ghosh, Bhogale, and Deshpande (2026+) <doi:10.48550/arXiv.2510.08204>.

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>.

Fast 'SVMlight' reader and writer. 
    'SVMlight' is most commonly used format for storing 
    sparse matrices (possibly with some target variable) on disk.
    For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.