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.

Implements the high-dimensional two-sample test 
    proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. 
    It also implements the test proposed by Srivastava, Katayama, 
    and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests 
    are particularly suitable to high dimensional data from two populations 
    for which the classical multivariate Hotelling's T-square test fails due 
    to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm 
    tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, 
    referred to as zwl_test() in this package, provide a reliable and powerful test.

Computes the scores and ranks candidates according to voting rules electing the highest median grade.
    Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>.
    Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.

Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, detect price jumps and investigate microstructure noise and intraday
    periodicity. A detailed vignette can be found in the open-access paper 
    "Analyzing Intraday Financial Data in R: The highfrequency Package" 
    by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).