diff --git a/DESCRIPTION b/DESCRIPTION
index 2942ee0..91aa442 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,8 +3,11 @@ Title: Statistical Package for Generating Alpha Shapes
 Version: 0.0.0.9000
 Authors@R: 
     person("Emily", "Winn-Nunez", , "emily_winn-nunez@brown.edu", role = c("aut", "cre"),
-           comment = c(ORCID = "0000-0001-6759-5406"))
+           comment = c(ORCID = "0000-0001-6759-5406"), person("Lorin", "Crawford",
+           email="lorin_crawford@brown.edu", role = "aut", 
+           comment = c(ORCID = "0000-0003-0178-8242")))
 Description:  Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The ashapesampler package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabalistic sampling from a known distrubtion, is the computational implementation of the theory derived in that paper. 
+Winn-Nunez et. al. (2024) 
 License: GPL (>=3)
 Imports:
     pracma,
diff --git a/README.md b/README.md
index 27d0052..7725a47 100644
--- a/README.md
+++ b/README.md
@@ -72,7 +72,9 @@ For macOS users, the Xcode Command Line Tools include a GCC compiler. Instructio
 
 ## R Package Installation
 
-To install the package, we recommend using the remotes package by running the command:
+Package will appear eventually on CRAN, at which time one can download the package there.
+
+To install the package from github, we recommend using the remotes package by running the command:
 
 	remotes::install_github('lcrawlab/ashapesampler')
 
@@ -105,7 +107,7 @@ Primate manibular molar data and neutrophil binary masks can be accessed and dow
 
 ## Relevant Citations
 
-E.T. Winn-Nuñez, H. Witt, D. Bhaskar, R. Huang, I.Y. Wong, J. Reichner, and L. Crawford. A probabilistic method for sampling alpha-shapes.
+E.T. Winn-Nuñez, H. Witt, D. Bhaskar, R. Huang, I.Y. Wong, J. Reichner, and L. Crawford. Generative modeling of biological shapes and images using a probabilistic α-shape sample. bioArxiv.
 
 ## Questions and Feedback