Package {heterogeneouspeereffects}

Title: Heterogeneous Peer Effect
Version: 0.1.0
Description: Heterogeneous Peer Effect Package provides two-step Generalized Method of Moments (GMM) estimators for heterogeneous peer effects in group-level treatment models developed by Pasquier, Rossi and Wang (2026) https://crest.science/wp-content/uploads/2026/09/2026-11.pdf. The package separates the direct effect of treatment from within-group and between-group spillover effects, using a cross-fitted, semiparametric approach that leaves the propensity score unspecified and estimates it nonparametrically. Two identification settings are implemented: one in which eligibility for treatment coincides with group identity, and one in which identity is orthogonal to eligibility, allowing peer effects to differ across subgroups (e.g. by gender). Point estimates, standard errors, and test statistics are returned for the direct effect and for each within- and between-group peer effect.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, ggplot2, purrr, flextable, caret, MASS, mvtnorm
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Author: Felix Pasquier [aut], Laurine Meier [aut, cre], Pauline Rossi [aut]
RoxygenNote: 7.3.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-28 10:09:17 UTC; laurinemeier
Maintainer: Laurine Meier <laurine.meier@ensae.fr>
URL: https://github.com/LaurineMir/heterogeneouspeereffects
BugReports: https://github.com/LaurineMir/heterogeneouspeereffects/issues
Repository: CRAN
Date/Publication: 2026-10-08 10:20:14 UTC

Two-Step GMM Estimation of Heterogeneous Peer Effects When Identity Equals Eligibility

Description

Two-Step GMM Estimation of Heterogeneous Peer Effects When Identity Equals Eligibility

Usage

heter_endo_gmm(YE, YN, D, s, n_param = 5, tol = 1e-06)

Arguments

YE

: average outcome for eligible individuals within a group (vector)

YN

average outcome for non-eligible individuals within a group (vector)

D

group binary treatment indicator (vector)

s

share of eligible individuals in the group (vector)

n_param

: number of parameters to estimate

tol

: convergency criteria of optimization

Value

A list with three elements:

estimates

data frame with the estimated values of the direct effect (delta), intra-group effect (theta_within), inter-group effect (theta_between) and their standard errors.

table

a flextable object formatting estimates for display.

plot

a ggplot object showing the treatment effects on eligible and non-eligible individuals across values of s.

Examples


# Parameters
delta = -3
thetaW = 0.7
thetaB = 0.3
beta_NE = -2
beta_E = -1
G <- 1000
# Generate the mean and sd of the random vector alpha
mu_alphaX <- c(8, 4, -3, 2)
Sigma_alphaX <- matrix(c(4, 1, 0, 0, 1, 4, 0, 0, 0, 0, 4, -4.2, 0, 0, -4.2, 9), nrow = 4)
# Create a logistic function to correlate D with s
logistic <- function(x) {
1 / (1 + exp(-x))
}
# Generate the alpha vector
alphaX <- mvtnorm::rmvnorm(n = G, mean = mu_alphaX, sigma = Sigma_alphaX)
# Scale s to control correlation strength (optional)
s <- runif(n = G)
scaled_s <- (s - mean(s)) / sd(s)  # Standardize s for better control
# Compute probabilities for D based on scaled s
prob_D <- logistic(scaled_s)  # Adjust scaling for desired correlation
# Generate D based on the probabilities
D <- rbinom(n = G, size = 1, prob = prob_D)
# Generate YE and YNE 
YN <- ((1 - thetaW * s ) * (alphaX[,1] + beta_NE * alphaX[,3])  +
thetaB * s * (alphaX[,2] + beta_E * alphaX[,4]) +
delta * thetaB * s)
YE <- ((1 - thetaW * s) * (1 - s) * thetaB * (alphaX[,1] + beta_NE * alphaX[,3]) +
(1 + thetaW * (s * (1 - s) * thetaW - 1)) * (alphaX[,2] + beta_E * alphaX[,4]) +
delta * (1 + thetaW * (s * (1 - s) * thetaW - 1)) * D) /
 ((1 - thetaW * s) * (1 - thetaW + s * (1 - s) * (thetaW^2 - thetaB^2)))
result_simple_5param <- heter_endo_gmm(YE, YN, D, s)
# Access the estimates data frame:
result_simple_5param$estimates
# Display the formatted table:
# print(result_simple_5param$table)
# Display the treatment effect plot:
# print(result_simple_5param$plot)

Two-Step GMM Estimation of Heterogeneous Peer Effects with Orthogonal Identity-Based Eligibility

Description

Two-Step GMM Estimation of Heterogeneous Peer Effects with Orthogonal Identity-Based Eligibility

Usage

ortho_heter_endo_gmm(YM, YF, D, sM, sEM, sEF, tol = 1e-06)

Arguments

YM

: average outcome for "male" individuals within a group (vector)

YF

average outcome for "female" individuals within a group (vector)

D

group binary treatment indicator (vector)

sM

share of "male" individuals in the group (vector)

sEM

share of eligible "male" individuals in the group (vector)

sEF

share of eligible "female" individuals in the group (vector)

tol

: convergency criteria of optimization

Value

A list with two elements:

estimates

data frame with the estimated values of the direct effect (delta), within effect among male (theta_within_M), within effect among female (theta_within_F), between effect from male to female (theta_between_F_M), between effect from female to male (theta_between_M_F) and their standard errors.

table

a flextable object formatting estimates for display.

Examples


delta = -3
thetaWM = 0.6
thetaWW = 0.2
thetaBWM = -0.5
thetaBMW = 0.5
beta_W = -2
beta_M = -1
G <- 500
# Generate the mean and sd of the random vector alpha
mu_alphaX <- c(8, 4, -3, 2)
Sigma_alphaX <- matrix(c(4, 1, 0, 0, 1, 4, 0, 0, 0, 0, 4, -4.2, 0, 0, -4.2, 9), nrow = 4)
# Create a logistic function to correlate D with s
logistic <- function(x) {
 1 / (1 + exp(-x))
}
# Generate the alpha vector
alphaX <- mvtnorm::rmvnorm(n = G, mean = mu_alphaX, sigma = Sigma_alphaX)
# Generate the vectors (s, T)
sM <- runif(n = G)
sWE <- runif(n = G)
sME <- runif(n = G)
D <- rbinom(n = G, size = 1, prob = 0.5)
# Generate YE and YNE 
YW <- ((1 - thetaWM * sM) * (alphaX[,2] + beta_M * alphaX[,4]) +
thetaBWM * sM * (alphaX[,1] + beta_W * alphaX[,3]) +
 D * delta * (sWE * (1 - thetaWM * sM) + sME * thetaBWM * sM)) /
 (1 - thetaWW * (1 - sM) - thetaWM * sM +
    sM * (1 - sM) * (thetaWW * thetaWM - thetaBWM * thetaBMW))
YM <- ((1 - thetaWW * (1 - sM)) * (alphaX[,1] + beta_M * alphaX[,3]) +
thetaBMW * (1 - sM) * (alphaX[,2] + beta_W * alphaX[,4]) +
 D * delta * (sWE * thetaBMW * (1 - sM) +
                sME * (1 - thetaWW * (1 - sM)))) /
 (1 - thetaWW * (1 - sM) - thetaWM * sM +
    sM * (1 - sM) * (thetaWW * thetaWM - thetaBWM * thetaBMW))
# Resultats analytique
result_ortho <- ortho_heter_endo_gmm(YM, YW, D, sM, sME, sWE)
result_ortho$estimates
# print(result_ortho$table)