Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

Machine learning hierarchical risk clustering portfolio allocation strategies. 
 The implemented methods are:
  Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059>.
  Hierarchical clustering-based asset allocation (Raffinot, 2017)  
  <DOI: 10.3905/jpm.2018.44.2.089>.
  Hierarchical equal risk contribution portfolio (Raffinot, 2018)
  <DOI: 10.2139/ssrn.3237540>.
  A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019)
  <https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf>.

Provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.

A wrapper for the 'Highcharts' library including
    shortcut functions to plot R objects. 'Highcharts'
    <https://www.highcharts.com/> is a charting library offering
    numerous chart types with a simple configuration syntax.

Tests for two high-dimensional population mean vectors. The user has the option to compute the asymptotic, the permutation or the bootstrap based p-value of the test. Some references are: Chen S.X. and Qin Y.L. (2010). <doi:10.1214/09-AOS716>, Cai T.T., Liu W., and Xia Y. (2014) <doi:10.1111/rssb.12034> and Yu X., Li D., Xue L. and Li, R. (2023) <doi:10.1080/01621459.2022.2061354>.