R/hettx-package.R

In hettx: Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

#' Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation
#'
#' This package implements methods developed by Ding, Feller, and Miratrix
#' (JRSS-B, 2016) "Randomization Inference for Treatment Effect Variation", for
#' validly testing whether there is unexplained variation in treatment effects
#' across observations. The package also implements methods introduced in Ding,
#' Feller, and Miratrix (JASA, 2019) "Decomposing Treatment Effect Variation", for
#' measuring the degree of treatment effect heterogeneity explained by
#' covariates. The package includes wrapper functions implementing the proposed
#' methods, as well as helper functions for analyzing and visualizing the
#' results of the tests.
#'
#' This package partially supported by the Institute of Education Sciences, U.S.
#' Department of Education, through Grant R305D150040. The opinions expressed
#' are those of the authors and do not represent views of the Institute or the
#' U.S. Department of Education.
#' 
#' Special thanks to Masha Bertling for some early work on documenting this project.
#'
#' @name hettx-package
#' @aliases hettx-package 
#' @docType package
#' @author Peng Ding, Avi Feller, Ben Fifield, and Luke Miratrix
#' 
#' Maintainer: Ben Fifield \email{benfifield@@gmail.com}
#' @references Ding, Peng, Avi Feller and Luke Miratrix. (2016) "Randomization Inference for Treatment Effect Variation", Journal of the Royal Statistical Society-Series B.
#' Ding, Peng, Avi Feller and Luke Miratrix. (2019) "Decomposing Treatment Effect Variation", Journal of the American Statistical Association.
#' @keywords package
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