From 825304472d030ff83a2a0da78a39c26e073e7ef3 Mon Sep 17 00:00:00 2001
From: Lucy D'Agostino McGowan <lucydagostino@gmail.com>
Date: Mon, 5 Sep 2022 09:00:16 -0400
Subject: [PATCH] update package doc

---
 R/tipr-package.R | 11 ++++++-----
 man/tipr.Rd      | 11 ++++++-----
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/R/tipr-package.R b/R/tipr-package.R
index 2cf7e29..7b27b92 100644
--- a/R/tipr-package.R
+++ b/R/tipr-package.R
@@ -6,15 +6,16 @@
 #' @docType package
 #' @references
 #'
-#' D'Agostino McGowan, L. (2018). Improving Modern Techniques of Causal
-#' Inference: Finite Sample Performance of ATM and ATO Doubly Robust Estimators,
-#' Variance Estimation for ATO Estimators, and Contextualized Tipping Point
-#' Sensitivity Analyses for Unmeasured Confounding. PhD thesis, Vanderbilt
-#' University.
+#' D'Agostino McGowan, L, (2022). tipr: An R package for sensitivity analyses
+#' for unmeasured confounders. Journal of Open Source Software, 7(77), 4495.
 #'
 #' VanderWeele, TJ, and Peng D (2017). Sensitivity Analysis in Observational
 #' Research: Introducing the E-Value. Ann Intern Med, 167(4), 268–74.
 #'
+#' Cinelli, C, & Hazlett, C (2020). Making sense of sensitivity: Extending
+#' omitted variable bias. Journal of the Royal Statistical Society: Series B
+#' (Statistical Methodology), 82(1), 39–67.
+#'
 #' Lin, DY, Psaty, BM, & Kronmal, RA. (1998). Assessing the sensitivity
 #' of regression results to unmeasured confounders in observational studies.
 #' Biometrics, 54(3), 948–963.
diff --git a/man/tipr.Rd b/man/tipr.Rd
index cb9c793..5414fae 100644
--- a/man/tipr.Rd
+++ b/man/tipr.Rd
@@ -8,15 +8,16 @@
 The tipr package.
 }
 \references{
-D'Agostino McGowan, L. (2018). Improving Modern Techniques of Causal
-Inference: Finite Sample Performance of ATM and ATO Doubly Robust Estimators,
-Variance Estimation for ATO Estimators, and Contextualized Tipping Point
-Sensitivity Analyses for Unmeasured Confounding. PhD thesis, Vanderbilt
-University.
+D'Agostino McGowan, L, (2022). tipr: An R package for sensitivity analyses
+for unmeasured confounders. Journal of Open Source Software, 7(77), 4495.
 
 VanderWeele, TJ, and Peng D (2017). Sensitivity Analysis in Observational
 Research: Introducing the E-Value. Ann Intern Med, 167(4), 268–74.
 
+Cinelli, C, & Hazlett, C (2020). Making sense of sensitivity: Extending
+omitted variable bias. Journal of the Royal Statistical Society: Series B
+(Statistical Methodology), 82(1), 39–67.
+
 Lin, DY, Psaty, BM, & Kronmal, RA. (1998). Assessing the sensitivity
 of regression results to unmeasured confounders in observational studies.
 Biometrics, 54(3), 948–963.