Title: Heterogeneity Measures in Meta-Analysis
Description: Assess the presence of statistical heterogeneity and quantify its impact in the context of meta-analysis. It includes test for heterogeneity as well as other statistical measures (R_b, I^2, R_I).

Title: Assessing Heterogeneity in the Utility of a Surrogate Marker
Description: Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.

Description: Functions for the fitting and summarizing of heteroscedastic t-regression.

Packaged: Wed May  4 16:36:34 2005; jtaylor

Title: Testing for a Treatment Effect Using a Heterogeneous Surrogate
Authors@R: c(person("Layla", "Parast", role = c("aut", "cre"), email = "parast@austin.utexas.edu"))
Description: Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.

Title: Heterogeneous Treatment Effects in Regression Analysis
person("Tymon", "Sloczynski", role = c("aut"), email = "tslocz@brandeis.edu"),
person("Mark", "McAvoy", role = c("cre"), email = "mcavoy@brandeis.edu"))
Description: Computes diagnostics for linear regression when treatment effects are heterogeneous.
The output of 'hettreatreg' represents ordinary least squares (OLS)
estimates of the effect of a binary treatment as a weighted average of the average treatment effect
on the treated (ATT) and the average treatment effect on the untreated (ATU).
The program estimates the OLS weights on these parameters, computes the associated model diagnostics,
and reports the implicit OLS estimate of the average treatment effect (ATE).

Title: Fisherian and Neymanian Methods for Detecting and Measuring
person("Peng", "Ding", email = "pengdingpku@berkeley.edu", role = "aut"),
person("Avi", "Feller", email = "afeller@berkeley.edu", role = "aut"),
person("Ben", "Fifield", email = "benfifield@gmail.com", role = c("aut", "cre")),
person("Luke", "Miratrix", email = "lmiratrix@g.harvard.edu", role = "aut")
Description: Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>,
for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.
Imports: quantreg, plyr, mvtnorm, MASS, foreach, parallel, doParallel,
moments, formula.tools, purrr, dplyr, tidyverse
Packaged: 2019-03-02 18:51:21 UTC; benfifield