A collection of functions to manage, to investigate and to analyze data sets of financial 
  assets from different points of view.

The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses. 
     It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).

R 'shiny' app to perform data analysis and visualization for the
    Fully Automated Senescence Test (FAST) workflow.

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',
    <https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2018.1518238>.
    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.