Fixing CRAN submission errors

.Rbuildignore

@@ -2,6 +2,7 @@
 ^\.Rproj\.user$
 Makefile
 README.Rmd
+README.html
 .github
 ^CODE_OF_CONDUCT\.md$
 ^app\.R$
@@ -13,3 +14,5 @@ Dockerfile
 
 # Adding the vignettes as these are heavy
 vignettes/
+LICENSE\.md
+\.vscode

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epiworldRShiny
 Type: Package
-Title: An 'RShiny' wrapper of the R package 'epiworldR'
+Title: An 'RShiny' Wrapper of the R Package 'epiworldR'
 Version: 0.1-0
 Date: 2024-05-14
 Authors@R: c(
@@ -17,10 +17,10 @@ Authors@R: c(
   person("Matthew", "Samore", role=c("ctb"), email="[email protected]", comment = c(ORCID = "0000-0002-4862-9196")),
   person("Jay", "Love", role=c("ctb"), email="[email protected]", comment = c(ORCID="0000-0002-9371-2466")),
   person("Kristina", "Stratford", role=c("ctb"), email="[email protected]"))
-Description: This 'RShiny' wrapper provides a user-friendly interface to the R package 'epiworldR'. Currently, the package features the SEIR equity, SIS, SIR, SEIR, SISD, SIRD, and SEIRD models, networked and fully connected versions Meyer et al. (2023) <DOI:10.21105/joss.05781> . Using 'epiworldRShiny', users can easily run simulations, visualize results interactively, and generate synthetic data.
-URL: https://github.com/UofUEpiBio/epiworldRShiny,
+Description: This 'RShiny' wrapper provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and the Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.
+URL: https://github.com/UofUEpiBio/epiworldRShiny/,
     https://uofuepibio.github.io/epiworldRShiny/,
-BugReports: https://github.com/UofUEpiBio/epiworldRShiny/issues
+BugReports: https://github.com/UofUEpiBio/epiworldRShiny/issues/
 License: MIT + file LICENSE
 RoxygenNote: 7.3.1
 Encoding: UTF-8
@@ -38,4 +38,3 @@ Imports:
     plotly
 Depends:
     shiny
-VignetteBuilder: knitr

LICENSE

@@ -1,2 +1,2 @@
-Copyright 2024
-Derek Meyer. All rights reserved.
+YEAR: 2024
+COPYRIGHT HOLDER: George G. Vega Yon

LICENSE.md

@@ -0,0 +1,21 @@
+# MIT License
+
+Copyright (c) 2024 George G. Vega Yon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

R/functions-server.R

@@ -14,7 +14,7 @@
 #' @return A matrix representing the generated population, with columns for age
 #' groups (0-19, 20-59, 60+), NotHispanic, and Female.
 #' @examples
-#' pop_generator(n = 10000, prop_hispanic = .5, prop_female = .5,
+#' pop_generator(n = 1000, prop_hispanic = .5, prop_female = .5,
 #'               prop_19_59_60plus = c(.3, .6))
 #' @export
 #' @family Server side functions

README.Rmd

@@ -16,7 +16,7 @@ This R package provides a user-friendly application for [epiworldR](https://gith
 - Built-in capability to define population and disease parameters.
 - Informative visualizations and tables after running each simulation.
 
-You can find more examples on the package's website: <https://uofuepibio.github.io/epiworldRShiny>
+You can find more examples on the package's website: <https://uofuepibio.github.io/epiworldRShiny/>
 
 ## Installation
 

README.md

@@ -9,23 +9,21 @@ status](https://www.r-pkg.org/badges/version/epiworldRShiny)](https://CRAN.R-pro
 <!-- badges: end -->
 
 This R package provides a user-friendly application for
-<a href="https://github.com/UofUEpiBio/epiworldR"
-target="_blank">epiworldR</a>, a wrapper of the C++ library
-<a href="https://github.com/UofUEpiBio/epiworld"
-target="_blank">epiworld</a>. It provides a general framework for
-modeling disease transmission using <a
-href="https://en.wikipedia.org/w/index.php?title=Agent-based_model&amp;oldid=1153634802"
-target="_blank">agent-based models</a>. Some of the main features
-include:
-
-- Fast simulation with an average of 30 million agents/day per second.
-- 9 different epidemiological models to choose from.
-- Built-in capability for user-defined interventions.
-- Built-in capability to define population and disease parameters.
-- Informative visualizations and tables after running each simulation.
+[epiworldR](https://github.com/UofUEpiBio/epiworldR), a wrapper of the
+C++ library [epiworld](https://github.com/UofUEpiBio/epiworld). It
+provides a general framework for modeling disease transmission using
+[agent-based
+models](https://en.wikipedia.org/w/index.php?title=Agent-based_model&oldid=1153634802).
+Some of the main features include:
+
+  - Fast simulation with an average of 30 million agents/day per second.
+  - 9 different epidemiological models to choose from.
+  - Built-in capability for user-defined interventions.
+  - Built-in capability to define population and disease parameters.
+  - Informative visualizations and tables after running each simulation.
 
 You can find more examples on the package’s website:
-<https://uofuepibio.github.io/epiworldRShiny>
+<https://uofuepibio.github.io/epiworldRShiny/>
 
 ## Installation
 
@@ -63,17 +61,13 @@ number, a model summary, and a table of counts over time are displayed.
 This example features: - SEIR network model for COVID-19  
 The day of peak infections occurs on day 12, maxing at about 18,000
 infections.  
-- The disease spreads rapidly at the simulation’s beginning, drastically
-decreasing over the first ten days.  
-- Model summary  
-- State counts table
-
-<figure>
-<img
-src="https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/f4e7d313-e3b6-4ebb-9c0a-ca4d53ef9cea"
-alt="example 1 GIF" />
-<figcaption aria-hidden="true">example 1 GIF</figcaption>
-</figure>
+\- The disease spreads rapidly at the simulation’s beginning,
+drastically decreasing over the first ten days.  
+\- Model summary  
+\- State counts table
+
+![example 1
+GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/f4e7d313-e3b6-4ebb-9c0a-ca4d53ef9cea)
 
 ### Example \#2
 
@@ -82,18 +76,15 @@ closure interventions to curb disease spread. All model output can be
 interpreted using the same logic from example \#1.
 
 Key features: - SEIRD network model for COVID-19  
-- Vaccine prevalence = 70%  
-- School closure prevalence = 50%  
-- Day of school closure implementation = 7  
-- Significantly decreased number of infections and deaths.  
-- The majority of the population recovered or was susceptible by day 30.
-
-<figure>
-<img
-src="https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/d5405162-f7fe-4a42-8a4c-e9a2ac31be73"
-alt="example 2 GIF" />
-<figcaption aria-hidden="true">example 2 GIF</figcaption>
-</figure>
+\- Vaccine prevalence = 70%  
+\- School closure prevalence = 50%  
+\- Day of school closure implementation = 7  
+\- Significantly decreased number of infections and deaths.  
+\- The majority of the population recovered or was susceptible by day
+30.
+
+![example 2
+GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/d5405162-f7fe-4a42-8a4c-e9a2ac31be73)
 
 ### Example \#3
 
@@ -107,9 +98,5 @@ population, 70% non-hispanic - 52% female population - 30% of population
 younger than 20 years old - 30% of population between 20 and 60 years
 old - 40% of population older than 60.
 
-<figure>
-<img
-src="https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/20aeb62d-cb42-4882-8577-a3406f167bca"
-alt="example 3 GIF" />
-<figcaption aria-hidden="true">example 3 GIF</figcaption>
-</figure>
+![example 3
+GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/20aeb62d-cb42-4882-8577-a3406f167bca)