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) <arXiv:1612.08468>), further effects plots, interaction
    strength, and variable contribution breakdown (Gosiewska and Biecek
    (2019) <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.

Provides functions for creating flashcard decks of terms and 
    definitions. This package creates HTML slides using 'revealjs' that can be 
    viewed in the 'RStudio' viewer or a web browser.  Users can create 
    flashcards from either existing built-in decks or create their own from CSV
    files or vectors of function names.