Tidies and performs preliminary analysis of 'Coursera' research
    export data. These export data can be downloaded by anyone who has classes
    on Coursera and wants to analyze the data. Coursera is one of the leading 
    providers of MOOCs and was launched in January 2012. With over 25 million 
    learners, Coursera is the most popular provider in the world being followed 
    by EdX, the MOOC provider that was a result of a collaboration between 
    Harvard University and MIT, with over 10 million users. Coursera has over 
    150 university partners from 29 countries and offers a total of 2000+ 
    courses from computer science to philosophy. Besides, Coursera offers 180+ 
    specialization, Coursera's credential system, and four fully online Masters 
    degrees. For more information about Coursera check Coursera's
    About page on <https://blog.coursera.org/about/>.

Uses data from the 'EPSG' Registry to look up suitable coordinate reference 
    system transformations for spatial datasets in R.  Returns a data frame with 'CRS' codes
    that can be used for 'CRS' transformation and mapping projects.  Please see 
    the 'EPSG' Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.

Provides methods for powering cluster-randomized trials with two co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Li et al. (2020) <doi:10.1111/biom.13212>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Yang et al. (2022) <doi:10.1111/biom.13692>.

Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" <arXiv:2201.08343>.

Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).