Addressing CRAN comments

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: epiworldRShiny
 Type: Package
-Title: An 'RShiny' Wrapper of the R Package 'epiworldR'
+Title: A 'shiny' Wrapper of the R Package 'epiworldR'
 Version: 0.1-0
-Date: 2024-05-14
+Date: 2024-05-31
 Authors@R: c(
   person("Derek", "Meyer", role=c("aut","cre"), 
   email="[email protected]", comment = c(ORCID = "0009-0005-1350-6988")),
@@ -17,7 +17,7 @@ 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 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.
+Description: R 'shiny' web apps for epidemiological Agent-Based Models. It 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 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/

R/app.R

@@ -14,7 +14,7 @@
 #' @importFrom stats aggregate as.formula reshape
 #' @importFrom utils write.csv packageVersion
 #'
-#' @return Loads and opens the RShiny app for the epiworldR package
+#' @return Loads and opens the R shiny app for the epiworldR package
 #' @param ... Currently ignored.
 #' @export
 #' @name epiworldRShiny

R/functions-ui.R

@@ -22,7 +22,7 @@ text_input_disease_name <- function(model_name) {
 #' @rdname epiworldrshiny-ui
 #' @export
 #' @examples
-#' # slider_prevalence("SEIRD")
+#' slider_prevalence("SEIRD")
 slider_prevalence <- function(model_name) {
   shiny::sliderInput(
     paste0(model_name, "_prevalence"),

README.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "epiworldRShiny: An RShiny Application for the epiworldR Package"
+title: "epiworldRShiny: A 'shiny' Wrapper of the R Package 'epiworldR'"
 output: github_document
 ---
 

README.md

@@ -1,4 +1,4 @@
-epiworldRShiny: An RShiny Application for the epiworldR Package
+epiworldRShiny: A ‘shiny’ Wrapper of the R Package ‘epiworldR’
 ================
 
 <!-- badges: start -->
@@ -9,18 +9,20 @@ status](https://www.r-pkg.org/badges/version/epiworldRShiny)](https://CRAN.R-pro
 <!-- badges: end -->
 
 This R package provides a user-friendly application for
-[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.
+<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.
 
 You can find more examples on the package’s website:
 <https://uofuepibio.github.io/epiworldRShiny/>
@@ -61,13 +63,17 @@ 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
-
-![example 1
-GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/f4e7d313-e3b6-4ebb-9c0a-ca4d53ef9cea)
+- 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>
 
 ### Example \#2
 
@@ -76,15 +82,18 @@ 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.
-
-![example 2
-GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/d5405162-f7fe-4a42-8a4c-e9a2ac31be73)
+- 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>
 
 ### Example \#3
 
@@ -98,5 +107,9 @@ 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.
 
-![example 3
-GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/20aeb62d-cb42-4882-8577-a3406f167bca)
+<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>