Provides methods for powering cluster-randomized trials with two continuous co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) <doi:10.1002/sim.70015>, Yang et al. (2022) <doi:10.1111/biom.13692>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Li et al. (2020) <doi:10.1111/biom.13212>.

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).

Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).

Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).

A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.se/libcurl/>).

Implements a modern, unified estimation strategy for common 
	mediation estimands (natural effects, organic effects, interventional effects, 
	and recanting twins) in combination with modified treatment policies as 
	described in Liu, Williams, Rudolph, and Díaz (2024) 
	<doi:10.48550/arXiv.2408.14620>. Estimation makes use of recent advancements 
	in Riesz-learning to estimate a set of required nuisance parameters with 
	deep learning. The result is the capability to estimate mediation effects with 
	binary, categorical, continuous, or multivariate exposures with 
	high-dimensional mediators and mediator-outcome confounders using machine 
	learning.