2018 -> 2023 (#165)

Author: erikcs

r-package/policytree/DESCRIPTION

@@ -20,7 +20,7 @@ Suggests:
     testthat (>= 3.0.4),
     DiagrammeR
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 LinkingTo:
     Rcpp,
     BH

r-package/policytree/R/data.R

@@ -1,6 +1,6 @@
 #' Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization
 #'
-#' The DGP from section 6.4.1 in Zhou, Athey, and Wager (2018):
+#' The DGP from section 6.4.1 in Zhou, Athey, and Wager (2023):
 #' There are \eqn{d=3} actions \eqn{(a_0,a_1,a_2)} which depend
 #' on 3 regions the covariates \eqn{X \sim U[0,1]^p} reside in. Observed outcomes:
 #' \eqn{Y \sim N(\mu_{a_i}(X_i), 4)}

r-package/policytree/man/gen_data_mapl.Rd

@@ -7,7 +7,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-This document provides a short introduction to the `policytree` package, with examples from Zhou, Athey and Wager (2018), and Athey and Wager (2021). The last section contains details on the runtime of `policy_tree`.
+This document provides a short introduction to the `policytree` package, with examples from Zhou, Athey and Wager (2023), and Athey and Wager (2021). The last section contains details on the runtime of `policy_tree`.
 
 ```{r, echo = FALSE, message = FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
@@ -47,7 +47,7 @@ abline(0, -1, lwd = 4, col = 4)
 
 # Ex. 2: Multi-action treatment effect estimation
 
-The following example is from the 3-action DGP from section 6.4.1 in [Zhou, Athey and Wager (2018)](https://arxiv.org/abs/1810.04778)
+The following example is from the 3-action DGP from section 6.4.1 in [Zhou, Athey and Wager](https://arxiv.org/abs/1810.04778)
 
 ```{r}
 n <- 10000