A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
    Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> 
    and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

Implements the AdaptiveImpute matrix completion
    algorithm of 'Intelligent Initialization and Adaptive Thresholding for
    Iterative Matrix Completion' <doi:10.1080/10618600.2018.1518238> as
    well as the specialized variant of 'Co-Factor Analysis of
    Citation Networks' <doi:10.1080/10618600.2024.2394464>.
    AdaptiveImpute is useful for embedding sparsely observed matrices,
    often out performs competing matrix completion algorithms, and
    self-tunes its hyperparameter, making usage easy.

Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) <doi:10.1111/sjos.12031>.

The 'fastai' <https://docs.fast.ai/index.html> library 
             simplifies training fast and accurate neural networks 
             using modern best practices. It is based on research 
             in to deep learning best practices undertaken 
             at 'fast.ai', including 'out of the box' support
             for vision, text, tabular, audio, time series, and 
             collaborative filtering models.

Performs backward elimination with similar syntax to 
    the stepAIC() function from the 'MASS' package. A bounding algorithm is 
    used to avoid fitting unnecessary models, making it much faster.

Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.