Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03>
    for a description and tutorial.

A wrapper around the 'quaqc' program described in Tremblay and
    Questa (2024) <doi:10.1093/bioinformatics/btae649>. 'quaqc' allows for
    assay for transposase-accessible chromatin using sequencing (ATAC-seq)
    specific quality control and read filtering of next-generation sequencing
    (NGS) data with minimal processing time and extremely low memory overhead.
    Any number of samples can be processed, using multiple threads if desired.
    'quaqc' outputs a comprehensive set of aligned read metrics, including
    alignment size, fragment size, percent duplicates, mapq scores, read depth,
    GC content, and others. Although designed for ATAC-seq data, 'quaqc' can
    also be used for other unspliced DNA sequencing experiments (such as
    chromatin immunoprecipitation sequencing, or ChIP-seq) as many of the
    metrics are related to general sequencing quality. This R package also
    provides additional utilities for custom analyses and plotting of 'quaqc'
    results.

Enables the user to calculate Value at Risk (VaR)
    and Expected Shortfall (ES) by means of various types of historical
    simulation. Currently plain-, age-, volatility-weighted- and filtered
    historical simulation are implemented in this package. Volatility weighting
    can be carried out via an exponentially weighted moving average model
    (EWMA) or other GARCH-type models. The performance can be assessed via
    Traffic Light Test, Coverage Tests and Loss Functions. The methods of the
    package are described in Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J.,
    Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.

An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.