Contains the core functions associated with Fast
        Regularized Canonical Correlation Analysis. Please see the following for
        details: Raul Cruz-Cano, Mei-Ling Ting Lee, 
        Fast regularized canonical correlation analysis, 
        Computational Statistics & Data Analysis, 
        Volume 70, 2014, Pages 88-100, 
        ISSN 0167-9473 <doi:10.1016/j.csda.2013.09.020>.

Provides implementation of statistical methods for random objects 
    lying in various metric spaces, which are not necessarily linear spaces. 
    The core of this package is Fréchet regression for random objects with 
    Euclidean predictors, which allows one to perform regression analysis 
    for non-Euclidean responses under some mild conditions. 
    Examples include distributions in 2-Wasserstein space, 
    covariance matrices endowed with power metric (with Frobenius metric 
    as a special case), Cholesky and log-Cholesky metrics, spherical data.  
    References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.

An R client for the 'Federal Reserve Economic Data'
    ('FRED') API <https://research.stlouisfed.org/docs/api/>.  Functions
    to retrieve economic time series and other data from 'FRED'.

Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <doi:10.48550/arXiv.2110.11074>.

The free algebra in R with non-commuting indeterminates.
     Uses 'disordR' discipline
     (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>).  To cite the
     package in publications please use Hankin (2022)
     <doi:10.48550/ARXIV.2211.04002>.

Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) 
    <doi:10.1093/biomet/asaa016>. This package relies on the optimization software 'MOSEK', 
    <https://www.mosek.com>.