Merge pull request #30 from lcrawlab/new_functions

Fixing description

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 1.0.0
-Date: 2024-01-25 23:49:33 UTC
-SHA: 1788ecfc6bda151fa4b2bff03590d76a0d5400a0
+Date: 2024-01-26 20:23:53 UTC
+SHA: 7056200a26dd2e526a66e29d2927a9f7fd5f212f

DESCRIPTION

@@ -1,12 +1,12 @@
 Package: ashapesampler
-Title: Statistical Package for Generating Alpha Shapes
+Title: Generating Alpha Shapes
 Version: 1.0.0
 Authors@R: 
     c(person("Emily", "Winn-Nunez", email="[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-6759-5406")), person("Lorin", "Crawford",
            email="[email protected]", 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, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al.(2024)<doi:10.1101/2024.01.09.574919>.
+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, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al.(2024)<doi:10.1101/2024.01.09.574919>.
 License: GPL (>=3)
 Imports:
     pracma,

cran-comments.md

@@ -1,3 +1,10 @@
+January 26, 2024
+
+The following edits have been made to the Description file as requested:
+* "Statistical Package for" removed from title for redundancy
+* Package names in Description are now in single quotation marks
+* doi link for relevant citations has been fixed
+
 January 25, 2024
 
 Typos in Description file fixed.