Functions to compute Wasserstein barycenters
    of subset posteriors using the swapping algorithm developed by Puccetti, 
    Rüschendorf and Vanduffel (2020) <doi:10.1016/j.jmaa.2017.02.003>. The 
    Wasserstein barycenter is a geometric approach for combining subset 
    posteriors. It allows for parallel and distributed computation of the 
    posterior in case of complex models and/or big datasets, thereby increasing
    computational speed tremendously.

R binding for 'libfswatch', a file system monitoring library.
    Watch files, or directories recursively, for changes in the
    background.  Log activity, or call an R function, upon every change
    event.

A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.

The main purpose of waterquality is to quickly and easily convert
    satellite-based reflectance imagery into one or many well-known water quality
    algorithms designed for the detection of harmful algal blooms or the following
    pigment proxies: chlorophyll-a, blue-green algae (phycocyanin), and turbidity.
    Johansen et al. (2019) <doi:10.21079/11681/35053>.

Provides Water Year Hydrologic Classification Indices based on measured 
    unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources
    and subject to revision.

Wrap-around Time Series (WATS) plots for interrupted time series
    designs with seasonal patterns.
    Longitudinal trajectories are shown in both Cartesian and polar coordinates.
    In many scenarios, a WATS plot more clearly shows the existence and effect size of
    of an intervention.
    This package accompanies
    "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs"
    by Rodgers, Beasley, & Schuelke (2014)
    <doi:10.1080/00273171.2014.946589>;
    see 'citation("Wats")' for details.