Provides statistical methods for analyzing experimental 
    evaluation of the causal impacts of algorithmic recommendations 
    on human decisions developed by Imai, Jiang, Greiner, Halen, and 
    Shin (2023) <doi:10.1093/jrsssa/qnad010>.
    The data used for this paper, and made available here, are interim, 
    based on only half of the observations in the study and (for those 
    observations) only half of the study follow-up period. We use them 
    only to illustrate methods, not to draw substantive conclusions.

Create a pie like plot to visualise if the aim or several aims of a
             project is achieved or close to be achieved i.e the aim is achieved when the point is at the
             center of the pie plot. Imagine it's like a dartboard and the center means 100%
             completeness/achievement. Achievement can also be understood as 100%
             coverage. The standard distribution of completeness allocated in the pie plot
             is 50%, 80% and 100% completeness.

Named after the Irish name for weather, this package contains 
    tidied data from the Irish Meteorological Service's hourly observations for 2017. 
    In all, the data sets include observations from 25 weather stations, and also
    latitude and longitude coordinates for each weather station. Now includes energy 
    generation data for Ireland and Northern Ireland (2017), including Wind Generation data.

A toolkit for archaeological time series and time intervals.
    This package provides a system of classes and methods to represent and
    work with archaeological time series and time intervals. Dates are
    represented as "rata die" and can be converted to (virtually) any
    calendar defined by Reingold and Dershowitz (2018)
    <doi:10.1017/9781107415058>. This packages offers a simple API that
    can be used by other specialized packages.

The 'AIPW' pacakge implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology". Visit: <https://yqzhong7.github.io/AIPW/> for more information.