Bipartite graph-based hierarchical clustering, developed for pharmacogenomic 
    datasets and datasets sharing the same data structure. The goal is to 
    construct a hierarchical clustering of groups of samples based on 
    association patterns between two sets of variables. In the context of
    pharmacogenomic datasets, the samples are cell lines, and the two sets of 
    variables are typically expression levels and drug sensitivity values. 
    For this method, sparse canonical correlation analysis from 
    Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> 
    is first applied to extract association patterns for each group of samples. 
    Then, a nuclear norm-based dissimilarity measure is used to construct a 
    dissimilarity matrix between groups based on the extracted associations. 
    Finally, hierarchical clustering is applied.

Estimates hierarchical F-statistics from haploid or
    diploid genetic data with any numbers of levels in the hierarchy, following the
    algorithm of Yang (Evolution(1998), 52:950).
    Tests via randomisations the significance
    of each F and variance components, using the likelihood-ratio statistics G
    (Goudet et al. (1996) <https://academic.oup.com/genetics/article/144/4/1933/6017091>).
    Estimates genetic diversity statistics
    for haploid and diploid genetic datasets in various formats, including inbreeding and
    coancestry coefficients, and population specific F-statistics following
    Weir and Goudet (2017) <https://academic.oup.com/genetics/article/206/4/2085/6072590>.

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 portfolio allocation strategies based on hierarchical clustering methods. 
 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>.