Implement the statistical test proposed in Weng et al. (2021) to test whether
    the average treatment effect curve is constant and whether a discrete covariate is a significant effect modifier.

A set of tools for relational and event analysis, including two- and one-mode network brokerage and structural measures, and helper functions optimized for relational event analysis with large datasets, including creating relational risk sets, computing network statistics, estimating relational event models, and simulating relational event sequences. For more information on relational event models, see Butts (2008) <doi:10.1111/j.1467-9531.2008.00203.x>, Lerner and Lomi (2020) <doi:10.1017/nws.2019.57>, Bianchi et al. (2024) <doi:10.1146/annurev-statistics-040722-060248>, and Butts et al. (2023) <doi:10.1017/nws.2023.9>. In terms of the structural measures in this package, see Leal (2025) <doi:10.1177/00491241251322517>, Burchard and Cornwell (2018) <doi:10.1016/j.socnet.2018.04.001>, and Fujimoto et al. (2018) <doi:10.1017/nws.2018.11>. This package was developed with support from the National Science Foundation’s (NSF) Human Networks and Data Science Program (HNDS) under award number 2241536 (PI: Diego F. Leal). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

Fits dose-response models utilizing a Bayesian model
  averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211>
  for both continuous and binary responses. Longitudinal dose-response modeling
  is also supported in a Bayesian model averaging framework as outlined in 
  Payne, Ray, and Thomann (2024)
  <doi:10.1080/10543406.2023.2292214>. Functions
  for plotting and calculating various posterior quantities
  (e.g. posterior mean, quantiles, probability of minimum efficacious dose,
  etc.) are also implemented.  Copyright Eli Lilly and Company (2019).

Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. 
    The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.