CRAN v0.4.0

r-package/RcausalEGM/.Rbuildignore

@@ -1,3 +1,4 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$
+^cran-comments\.md$

r-package/RcausalEGM/DESCRIPTION

@@ -2,14 +2,14 @@ Package: RcausalEGM
 Type: Package
 Title: A General Causal Inference Framework by Encoding Generative Modeling
 Version: 0.4.0
-Date: 2024-05-14
+Date: 2024-05-15
 Authors@R: c(
- person("Qiao", "Liu", role = c("aut", "cre"), email = "[email protected]"),
+ person("Qiao", "Liu", role = c("cre"), email = "[email protected]"),
  person("Wing", "Wong", role = "aut"),
  person("Balasubramanian", "Narasimhan", role = "ctb")
  )
 Maintainer: Qiao Liu <[email protected]>
-Description: CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <arXiv:2212.05925>.
+Description: CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu et al (2022) <doi:10.48550/arXiv.2212.05925>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Imports: reticulate
@@ -23,5 +23,8 @@ Suggests:
  rmarkdown,
  knitr,
  testthat (>= 3.0.0)
+Author: Qiao Liu [cre],
+ Wing Wong [aut],
+ Balasubramanian Narasimhan [ctb]
 VignetteBuilder: knitr
 Config/testthat/edition: 3

r-package/RcausalEGM/R/causalegm.R

@@ -60,7 +60,6 @@ install_causalegm <- function(method = "auto",pip = TRUE) {
 #' x <- rbinom(n, 1, 0.4 + 0.2 * (v[, 1] > 0))
 #' y <- pmax(v[, 1], 0) * x + v[, 2] + pmin(v[, 3], 0) + rnorm(n)
 #' model <- causalegm(x=x, y=y, v=v, n_iter=3000)
-#' paste("The average treatment effect (ATE):", round(model$ATE, 2))
 #' }
 #'
 #' @importFrom reticulate py_module_available