Miscellaneous R functions for calculating and decomposing hierarchical diversity metrics, including hierarchical allele richness, hierarchical exponential Shannon entropy (true diversity of order q=1), hierarchical heterozygosity and genetic differentiation (Jaccard dissimilarity, Delta D, Fst and Jost's D). In addition,a new approach to identify population structure based on the homogeneity of multivariate variances of Shannon differentiation is presented. This package allows users to analyse spatial structured genetic data or species data under a unifying framework (Gaggiotti, O. E. et al, 2018, Evol Appl, 11:1176-1193; <DOI:10.1111/eva.12593>), which partitions diversity and differentiation into any hierarchical levels. It helps you easily structure and format your data. In summary,it implements the analyses of true diversity profiles (q=0, 1, 2), hierarchical diversities and differentiation decomposition, visualization of population structure, as well as the estimation of correlation between geographic distance and genetic differentiation.

A graphical user interface for calculating power and sample size
for hierarchical data
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
XMLRPC, RCurl, bitops, XML, tcltk, tcltk2

Hierarchical Clustering-Based Portfolio Allocation Strategies
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> and
Hierarchical clustering-based asset allocation (Raffinot, 2017)
<DOI: 10.3905/jpm.2018.44.2.089>.

Outlier Detection Algorithms for High-Dimensional Data
foreach, DMwR, plyr, proxy, FNN, ggplot2
Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].