Description updated

sokbae · Oct 20, 2023 · 144439e · 144439e
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -6,3 +6,4 @@
 ^\.travis\.yml$
 ^CRAN-RELEASE$
 ^.github$
+^CRAN-SUBMISSION$
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 0.3.0
+Date: 2023-10-20 15:50:52 UTC
+SHA: 10e7a20d294d74b6cca6679230e0ec72679fde03
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -6,7 +6,7 @@ Authors@R: c(
     person("Sung Jae", "Jun", email = "suj14@psu.edu", role = "aut"),
     person("Sokbae", "Lee", email = "sl3841@columbia.edu", role = c("aut", "cre")))
 Description: Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies
-  under the monotone treatment response (MTR) and monotone treatment selection (MTS) assumptions.
+  under the monotone treatment response and monotone treatment selection assumptions.
   For more details, see the paper by Jun and Lee (2023), 
   "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," <arXiv:2004.08318 [econ.EM]>,
   accepted for publication in Journal of Business & Economic Statistics.

diff --git a/README.Rmd b/README.Rmd
@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/sokbae/ciccr/workflows/R-CMD-check/badge.svg)](https://github.com/sokbae/ciccr/actions)
 [![](https://cranlogs.r-pkg.org/badges/ciccr)]( https://CRAN.R-project.org/package=ciccr)
-[![codecov](https://codecov.io/gh/sokbae/ciccr/branch/master/graph/badge.svg?token=WZ348KLQGA)](https://codecov.io/gh/sokbae/ciccr)
+[![codecov](https://codecov.io/gh/sokbae/ciccr/branch/master/graph/badge.svg?token=WZ348KLQGA)](https://app.codecov.io/gh/sokbae/ciccr)
 <!-- badges: end -->
 
 The goal of ciccr is to implement methods for carrying out causal inference in case-control and case-population studies ([Jun and Lee, 2023](https://arxiv.org/abs/2004.08318)).

diff --git a/README.md b/README.md
@@ -7,7 +7,7 @@
 
 [![R-CMD-check](https://github.com/sokbae/ciccr/workflows/R-CMD-check/badge.svg)](https://github.com/sokbae/ciccr/actions)
 [![](https://cranlogs.r-pkg.org/badges/ciccr)](https://CRAN.R-project.org/package=ciccr)
-[![codecov](https://codecov.io/gh/sokbae/ciccr/branch/master/graph/badge.svg?token=WZ348KLQGA)](https://codecov.io/gh/sokbae/ciccr)
+[![codecov](https://codecov.io/gh/sokbae/ciccr/branch/master/graph/badge.svg?token=WZ348KLQGA)](https://app.codecov.io/gh/sokbae/ciccr)
 <!-- badges: end -->
 
 The goal of ciccr is to implement methods for carrying out causal

diff --git a/cran-comments.md b/cran-comments.md
@@ -1,5 +1,6 @@
 ## R CMD check results
-There were no ERRORs or WARNINGs. 
+* There were no ERRORs or WARNINGs. 
+* The spelling of "Monotonicity" is correct. 
 
 ## This version
 This version includes the following updates:

diff --git a/man/ciccr-package.Rd b/man/ciccr-package.Rd
diff --git a/tests/testthat/Rplots-RR.pdf b/tests/testthat/Rplots-RR.pdf
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
diff --git a/tests/testthat/test-ciccr.R b/tests/testthat/test-ciccr.R
@@ -457,3 +457,16 @@ test_that("AR: The results should be different between FG and FG_CC", {
   expect_false(sum((results_rs$est != results_cc$est[2]))==0)
 
 })
+
+test_that("The results for avg_AR_logit under random sampling should be different between interaction = TRUE and FALSE", {
+
+  y = ACS_CC$topincome
+  t = ACS_CC$baplus
+  x = ACS_CC$age
+  results1 = avg_AR_logit(y, t, x, sampling = 'rs', interaction = FALSE)
+  results2 = avg_AR_logit(y, t, x, sampling = 'rs', interaction = TRUE)
+
+  expect_false(sum((results1$est != results2$est))==0)
+
+})
+