Weighted Piecewise Kernel Density Estimation for large data.

Data from the United Nation's World Population Prospects 2008

Data from the United Nation's World Population Prospects
        2010

Data from the United Nation's World Population Prospects 2012

Provides data from the United Nation's World Population Prospects 2015.

Provides data from the United Nation's World Population Prospects 2017.

Provides data from the United Nation's World Population Prospects 2019.

Explore data in the 'wpp2019' (or 2017, 2015, ...) package using a 'shiny' interface.

Performs Wasserstein projections from the predictive distributions of any model into the space of predictive distributions of linear models. We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) <doi:10.48550/arXiv.2012.09999>.

Estimate and plot wavelet quantile correlations(Kumar and Padakandla,2022) between two time series. Wavelet quantile correlation is used to capture the dependency between two time series across quantiles and different frequencies. This method is useful in identifying potential hedges and safe-haven instruments for investment purposes. See Kumar and Padakandla(2022) <doi:10.1016/j.frl.2022.102707> for further details.

Functions to assist in the processing and
    exploration of data from environmental monitoring programs.
    The package name stands for "water quality" and reflects the
    original focus on time series data for physical and chemical
    properties of water, as well as the biota. Intended for
    programs that sample approximately monthly, quarterly or
    annually at discrete stations, a feature of many legacy data
    sets. Most of the functions should be useful for analysis of
    similar-frequency time series regardless of the subject
    matter.

The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) <doi:10.1029/2022EF003350>. The package relies on two packages for continuous wavelet transforms: 'WaveletComp', which can be installed automatically, and 'wmtsa', which is optional and available from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/wmtsa/>. Users need to manually install 'wmtsa' from this archive if they prefer to use 'wmtsa' based decomposition.