A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier
    from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
    those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
    al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
    <doi:10.1093/bioinformatics/btr690>.

Computation of sparse eigenvectors of a matrix (aka sparse PCA)
    with running time 2-3 orders of magnitude lower than existing methods and
    better final performance in terms of recovery of sparsity pattern and 
    estimation of numerical values. Can handle covariance matrices as well as 
    data matrices with real or complex-valued entries. Different levels of 
    sparsity can be specified for each individual ordered eigenvector and the 
    method is robust in parameter selection. See vignette for a detailed 
    documentation and comparison, with several illustrative examples. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.

Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.

Provides a general framework for performing sparse functional
    clustering as originally described in Floriello and Vitelli (2017)
    <doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling
    data misalignment (see Vitelli, 2019, <doi:10.48550/arXiv.1912.00687>).

Implements sparse generalized factor models (sparseGFM) for dimension reduction
    and variable selection in high-dimensional data with automatic adaptation to weak factor
    scenarios. The package supports multiple data types (continuous, count, binary) through
    generalized linear model frameworks and handles missing values automatically. It provides
    12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso),
    adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso,
    and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features
    include cross-validation for regularization parameter selection using Sparsity Information
    Criterion (SIC), automatic determination of the number of factors via multiple information
    criteria, and specialized algorithms for row-sparse loading structures. The methodology
    employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability
    constraints and is particularly effective for high-dimensional applications in genomics, economics,
    and social sciences where interpretable sparse dimension reduction is crucial.
    For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.