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) <arXiv:2012.09999>.

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.

Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.

Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. (2021) <https://CRAN.R-project.org/package=gWQS>). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. (2015) <doi:10.1007/s13253-014-0180-3>). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. (2022) <doi:10.1289/EHP10570>).