Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output. For more information, please refer to <https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.

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

Fast implementation of hierarchical clustering

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.