[skip_vbump]v0.2.0 Release sdtm.oak 0.2.0 (#123)

* CRAN checklist

* CRAN checklist comments

* Update version

Author: rammprasad

.Rbuildignore

@@ -17,3 +17,4 @@
 ^inst/ct/README.md$
 ^NOTICE.md$
 ^tmp$
+^cran-comments\.md$

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: sdtm.oak
 Type: Package
 Title: SDTM Data Transformation Engine
-Version: 0.1.1
+Version: 0.2.0
 Authors@R: c(
     person("Rammprasad", "Ganapathy", email = "ganapathy.rammprasad@gene.com",
     role = c("aut", "cre")),
@@ -41,8 +41,8 @@ Language: en-US
 License: Apache License (>= 2)
 Copyright: F. Hoffmann-La Roche AG, Pattern Institute,
     Atorus Research LLC and Transition Technologies Science sp. z o.o.
-BugReports: https://github.com/pharmaverse/sdtm.oak/issues/
-URL: https://pharmaverse.github.io/sdtm.oak/
+BugReports: https://github.com/pharmaverse/sdtm.oak/issues
+URL: https://pharmaverse.github.io/sdtm.oak/, https://github.com/pharmaverse/sdtm.oak
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)

README.md

@@ -1,25 +1,19 @@
----
-editor_options: 
-  markdown: 
-    wrap: 72
----
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# sdtm.oak <a href="https://pharmaverse.github.io/sdtm.oak/"><img src="man/figures/logo.svg" align="right" height="139"/></a>
+# sdtm.oak <a href="https://pharmaverse.github.io/sdtm.oak/"><img src="man/figures/logo.svg" align="right" height="139" /></a>
 
 <!-- badges: start -->
 
 [![CRAN
 status](https://www.r-pkg.org/badges/version/sdtm.oak)](https://CRAN.R-project.org/package=sdtm.oak)
-
 <!-- badges: end -->
 
 An EDC (Electronic Data Capture systems) and Data Standard agnostic
 solution that enables the pharmaceutical programming community to
 develop CDISC (Clinical Data Interchange Standards Consortium) SDTM
 (Study Data Tabulation Model) datasets in R. The reusable algorithms
-concept in 'sdtm.oak' provides a framework for modular programming. We
+concept in ‘sdtm.oak’ provides a framework for modular programming. We
 plan to develop a code generation feature based on a standardized SDTM
 specification format, which has the potential to automate the creation
 of SDTM datasets.
@@ -32,7 +26,7 @@ The package is available from CRAN and can be installed with:
 install.packages("sdtm.oak")
 ```
 
-You can install the development version of 'sdtm.oak' from
+You can install the development version of `{sdtm.oak}` from
 [GitHub](https://github.com/pharmaverse/sdtm.oak/) with:
 
 ``` r
@@ -42,20 +36,20 @@ remotes::install_github("pharmaverse/sdtm.oak")
 
 ## Challenges with SDTM at the Industry Level
 
--   Raw Data Structure: Data from different EDC systems come in varying
-    structures, with different variable names, dataset names, etc.
+- Raw Data Structure: Data from different EDC systems come in varying
+  structures, with different variable names, dataset names, etc.
 
--   Varying Data Collection Standards: Despite the availability of CDASH
-    (Clinical Data Acquisition Standards Harmonization), pharmaceutical
-    companies still create different eCRFs using CDASH standards.
+- Varying Data Collection Standards: Despite the availability of CDASH
+  (Clinical Data Acquisition Standards Harmonization), pharmaceutical
+  companies still create different eCRFs using CDASH standards.
 
 Due to the differences in raw data structures and data collection
 standards, it may seem impossible to develop a common approach for
 programming SDTM datasets.
 
 ## GOAL
 
-'sdtm.oak' aims to address this issue by providing an EDC-agnostic,
+‘sdtm.oak’ aims to address this issue by providing an EDC-agnostic,
 standards-agnostic solution. It is an open-source R package that offers
 a framework for the modular programming of SDTM in R. With future
 releases; we plan to develop a code generation feature based on a
@@ -64,7 +58,7 @@ automate the creation of SDTM datasets.
 
 ## Scope
 
-Our goal is to use 'sdtm.oak' to program most of the domains specified
+Our goal is to use ‘sdtm.oak’ to program most of the domains specified
 in SDTMIG (Study Data Tabulation Model Implementation Guide: Human
 Clinical Trials) and SDTMIG-AP (Study Data Tabulation Model
 Implementation Guide: Associated Persons). This R package is based on
@@ -73,64 +67,68 @@ carrying out the SDTM mappings for any domains listed in the CDISC
 SDTMIG and across different versions of SDTM IGs. The design of these
 functions allows users to specify a raw dataset and a variable name(s)
 as parameters, making it EDC (Electronic Data Capture) agnostic. As long
-as the raw dataset and variable name(s) exist, 'sdtm.oak' will execute
+as the raw dataset and variable name(s) exist, ‘sdtm.oak’ will execute
 the SDTM mapping using the selected function. It’s important to note
-that 'sdtm.oak' may not handle sponsor-specific details related to
+that ‘sdtm.oak’ may not handle sponsor-specific details related to
 managing metadata for LAB tests, unit conversions, and coding
 information, as many companies have unique business processes.
 
 ## This Release
 
-With the V0.2.0 release of 'sdtm.oak' users can now efficiently
-create the DM domain and various SDTM domains, encompassing Findings,
-Events, Findings About, and Intervention classes. However, the V0.2.0 release does NOT cover Trial Design Domains, SV (Subject Visits), SE (Subject Elements), RELREC (Related Records), Associated Person domains, or the EPOCH Variable across all domains.
+With the V0.2.0 release of ‘sdtm.oak’ users can now efficiently create
+the DM domain and various SDTM domains, encompassing Findings, Events,
+Findings About, and Intervention classes. However, the V0.2.0 release
+does NOT cover Trial Design Domains, SV (Subject Visits), SE (Subject
+Elements), RELREC (Related Records), Associated Person domains, or the
+EPOCH Variable across all domains.
 
 ## Road Map
 
 Subsequent Releases: We are planning to develop the below features in
-the subsequent releases.\
+the subsequent releases.  
 - Metadata driven code generation based on the standardized SDTM
-specification.\
-- Functions required to program the Domains SV (Subject Visits), SE (Subject Elements) and the EPOCH Variable.\
-- Functions to derive standard units and results based on metadata. 
-- Additional features to be developed based on the user feedback.
+specification.  
+- Functions required to program the Domains SV (Subject Visits), SE
+(Subject Elements) and the EPOCH Variable.  
+- Functions to derive standard units and results based on metadata. -
+Additional features to be developed based on the user feedback.
 
 ## References and Documentation
 
--   Please go to
-    [Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html)
-    article to learn about Algorithms.
--   Please go to [Create Events
-    Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html)
-    to learn about step by step process to create an Events domain.
--   Please go to [Create Findings
-    Domain](https://pharmaverse.github.io/sdtm.oak/articles/findings_domain.html)
-    to learn about step by step process to create a Findings domain.
--   Please go to [Path to
-    Automation](https://pharmaverse.github.io/sdtm.oak/articles/study_sdtm_spec.html)
-    to learn about how the foundational release sets up the stage for
-    automation.
--   Please watch this YouTube video to learn about using the package
-    [YouTube
-    Video](https://www.youtube.com/watch?v=H0FdhG9_ttU&list=PLMtxz1fUYA5C67SvhSCINluOV2EmyjKql&index=3&ab_channel=RinPharma%5D)
--   RinPharma Virtual workshop
-    [slides](https://pharmaverse.github.io/rinpharma-2024-SDTM-workshop/)
+- Please go to
+  [Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html)
+  article to learn about Algorithms.
+- Please go to [Create Events
+  Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html)
+  to learn about step by step process to create an Events domain.
+- Please go to [Create Findings
+  Domain](https://pharmaverse.github.io/sdtm.oak/articles/findings_domain.html)
+  to learn about step by step process to create a Findings domain.
+- Please go to [Path to
+  Automation](https://pharmaverse.github.io/sdtm.oak/articles/study_sdtm_spec.html)
+  to learn about how the foundational release sets up the stage for
+  automation.
+- Please watch this YouTube video to learn about using the package
+  [YouTube
+  Video](https://www.youtube.com/watch?v=H0FdhG9_ttU&list=PLMtxz1fUYA5C67SvhSCINluOV2EmyjKql&index=3&ab_channel=RinPharma%5D)
+- RinPharma Virtual workshop
+  [slides](https://pharmaverse.github.io/rinpharma-2024-SDTM-workshop/)
 
 ## Feedback
 
-We ask users to follow the mentioned approach and try 'sdtm.oak' to map
+We ask users to follow the mentioned approach and try ‘sdtm.oak’ to map
 any SDTM domains supported in this release. Users can also utilize the
 test data in the package to become familiar with the concepts before
 attempting on their own data. Please get in touch with us using one of
 the recommended approaches listed below:
 
--   [Slack](https://oakgarden.slack.com/)
--   [GitHub](https://github.com/pharmaverse/sdtm.oak/issues)
+- [Slack](https://oakgarden.slack.com/)
+- [GitHub](https://github.com/pharmaverse/sdtm.oak/issues)
 
 ## Acknowledgments
 
 We thank the contributors and authors of the package. We also thank the
-CDISC COSA for sponsoring the 'sdtm.oak'. Additionally, we would like to
+CDISC COSA for sponsoring the ‘sdtm.oak’. Additionally, we would like to
 sincerely thank the volunteers from Roche, Pfizer, GSK, Vertex, and
 Merck for their valuable input as integral members of the CDISC COSA -
 OAK leadership team.

cran-comments.md

@@ -0,0 +1,6 @@
+## R CMD check results
+
+0 errors | 0 warnings | 0 note
+
+* We do not expect any errors, warnings, note.
+* This is v0.2.0 release of the package.