Allows R users to retrieve and parse data from the Urban 
    Institute's Education Data API <https://educationdata.urban.org/> into a 
    'data.frame' for analysis.

Provides a comprehensive collection of datasets related to education, 
    covering topics such as student performance, learning methods, test scores, 
    absenteeism, and other educational metrics. This package serves as a resource 
    for educational researchers, data analysts, and statisticians to explore and 
    analyze data in the field of education.

Easily compute education inequality measures and the distribution 
  of educational attainments for any group of countries, using the data set 
  developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. 
  The package offers the possibility to compute not only the Gini index, but 
  also generalized entropy measures for different values of the sensitivity 
  parameter. In particular, the package includes functions to compute the 
  mean log deviation, which is more sensitive to the bottom part of the 
  distribution; the Theil’s entropy measure, equally sensitive to all parts 
  of the distribution; and finally, the GE measure when the sensitivity 
  parameter is set equal to 2, which gives more weight to differences in 
  higher education. The decomposition of these measures in the components 
  between-country and within-country inequality is also provided. Two 
  graphical tools are also provided, to analyse the evolution of the
  distribution of educational attainments: The cumulative distribution 
  function and the Lorenz curve.

Implements the Edwards (1997) <doi:10.1002/j.1551-8833.1997.tb08229.x>
    Langmuir-based semi-empirical coagulation model, which predicts the concentration
    of organic carbon remaining in water after treatment with an Al- or Fe-based
    coagulant. Data and methods are provided to optimise empirical coefficients.

This is a data-only package containing model objects that predict
    human energy expenditure from wearable sensor data. Supported methods include
    the neural networks of Montoye et al. (2017) <doi:10.1080/1091367X.2017.1337638>
    and the models of Staudenmayer et al. (2015) <doi:10.1152/japplphysiol.00026.2015>,
    one a linear model and the other a random forest. The package is intended as
    a spoke for the hub-package 'accelEE', which brings together the above methods
    and others from packages such as 'Sojourn' and 'TwoRegression.'