Provides implementations of a family of Lasso variants,
    including Dantzig Selector, LAD Lasso, SQRT Lasso, and Lq Lasso, for
    estimating high-dimensional sparse linear models. We adopt the
    alternating direction method of multipliers and convert the original
    optimization problem into a sequence of L1-penalized least-squares
    minimization problems that are efficiently solved by linearization and
    multi-stage screening. In addition to sparse linear model estimation, we
    provide extensions of these methods to sparse Gaussian graphical model
    estimation, including TIGER and CLIME, using either L1 or adaptive
    penalties. Missing values can be tolerated for Dantzig selector and
    CLIME. Computation is memory-optimized using sparse matrix output. For
    more information, see
    <https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.

Create a flip over style Flash Card with desired data frame for Shiny application.

A fast implementation of hierarchical clustering that incorporates original code from Fionn Murtagh.

Methods for matrix factorization based on Wang and Stephens (2021) 
    <https://jmlr.org/papers/v22/20-589.html>.

Shed light on black box machine learning models by the help
    of model performance, variable importance, global surrogate models,
    ICE profiles, partial dependence (Friedman J. H. (2001)
    <doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W.
    (2016) <doi:10.48550/arXiv.1612.08468>), further effects plots,
    interaction strength, and variable contribution breakdown (Gosiewska
    and Biecek (2019) <doi:10.48550/arXiv.1903.11420>).  All tools are
    implemented to work with case weights and allow for stratified
    analysis.  Furthermore, multiple flashlights can be combined and
    analyzed together.

A fast and scalable linear mixed-effects model (LMM) estimation algorithm 
    for analysis of single-cell differential expression. The algorithm uses 
    summary-level statistics and requires less computer memory to fit the LMM.