JRaviLab · eboyer221 · May 27, 2026 · May 22, 2026
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: amR
-Title: amR, an R package suite to predict AMR in bacterial pathogens (amRdata, amRml, amRshiny)
+Title: amR, an R package suite to predict AMR in bacterial pathogens (amRdata, amRml, amRviz)
 Version: 0.0.0.9000
 Authors@R: c(
     person("Janani", "Ravi", , "janani.ravi@cuanschutz.edu", role = c("aut", "cre"),
@@ -12,7 +12,7 @@ Authors@R: c(
     person("Raymond", "Lesiyon", , "raymond.lesiyon@cuanschutz.edu", role = "ctb"),
     person("David", "Mayer", , "david.mayer@cuanschutz.edu", role = "ctb")
     )
-Description: amR is a metapackage aka package suite that lets users install the 3 packages, amRdata, amRml, and amRshiny with a single install function.
+Description: amR is a metapackage aka package suite that lets users install the 3 packages, amRdata, amRml, and amRviz with a single install function.
 License: BSD_3_clause + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

diff --git a/R/install_amR.R b/R/install_amR.R
@@ -1,10 +1,10 @@
 #' Install amR package suite
 #'
 #' Installs all packages in the amR suite for antimicrobial resistance
-#' prediction: amRdata, amRml, and amRshiny.
+#' prediction: amRdata, amRml, and amRviz.
 #'
 #' @param packages Character vector specifying which packages to install.
-#'   Options are "data", "ml", and "shiny". Default installs all three.
+#'   Options are "data", "ml", and "viz". Default installs all three.
 #' @param force Logical. If TRUE, forces reinstallation even if packages
 #'   are already installed. Default is FALSE.
 #' @param upgrade Character. Controls whether to upgrade dependencies.
@@ -31,13 +31,13 @@
 #' # Install specific packages quietly
 #' installAMR(packages = c("data", "ml"), quiet = TRUE)
 #' }
-installAMR <- function(packages = c("data", "ml", "shiny"),
+installAMR <- function(packages = c("data", "ml", "viz"),
                        force = FALSE,
                        upgrade = "default",
                        quiet = FALSE) {
   # Validate packages argument
 
-  valid_packages <- c("data", "ml", "shiny")
+  valid_packages <- c("data", "ml", "viz")
   packages <- match.arg(packages, valid_packages, several.ok = TRUE)
 
 
@@ -46,7 +46,7 @@
   repo_map <- c(
     data = "JRaviLab/amRdata",
     ml = "JRaviLab/amRml",
-    shiny = "JRaviLab/amRshiny"
+    viz = "JRaviLab/amRviz"
   )
 
   # Install each requested package

diff --git a/README.Rmd b/README.Rmd
@@ -33,7 +33,7 @@ The amR suite consists of three packages that work together:
 |---------|-------------|------------|
 | **amRdata** | Data curation and feature extraction from bacterial genomes | [JRaviLab/amRdata](https://github.com/JRaviLab/amRdata) |
 | **amRml** | Machine learning models for AMR prediction | [JRaviLab/amRml](https://github.com/JRaviLab/amRml) |
-| **amRshiny** | Interactive dashboard for exploring results | [JRaviLab/amRshiny](https://github.com/JRaviLab/amRshiny) |
+| **amRviz** | Interactive dashboard for exploring results | [JRaviLab/amRviz](https://github.com/JRaviLab/amRviz) |
 
 ## Installation
 
@@ -58,7 +58,7 @@ You can also install packages individually:
 ```r
 remotes::install_github("JRaviLab/amRdata")
 remotes::install_github("JRaviLab/amRml")
-remotes::install_github("JRaviLab/amRshiny")
+remotes::install_github("JRaviLab/amRviz")
 ```
 
 ## Quick start
@@ -67,23 +67,23 @@ remotes::install_github("JRaviLab/amRshiny")
 # Load all packages
 library(amRdata)
 library(amRml)
-library(amRshiny)
+library(amRviz)
 
 # 1. Prepare data with amRdata
 # features <- prepareFeatures(...)
 
 # 2. Train ML models with amRml
 # results <- runMLPipeline(...)
 
-# 3. Explore results with amRshiny
+# 3. Explore results with amRviz
 # launchDashboard(...)
 ```
 
 ## Workflow overview
 
 ```
 ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
-│  amRdata    │ --> │   amRml     │ --> │  amRshiny   │
+│  amRdata    │ --> │   amRml     │ --> │  amRviz   │
 │             │     │             │     │             │
 │ - Genomes   │     │ - Train LR  │     │ - Dashboard │
 │ - Features  │     │ - Evaluate  │     │ - Plots     │
@@ -95,7 +95,7 @@ library(amRshiny)
 
 - [amRdata documentation](https://jravilab.github.io/amRdata)
 - [amRml documentation](https://jravilab.github.io/amRml)
-- [amRshiny documentation](https://jravilab.github.io/amRshiny)
+- [amRviz documentation](https://jravilab.github.io/amRviz)
 
 ## Citation
 

diff --git a/README.md b/README.md
@@ -13,7 +13,7 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 the amR suite of packages for antimicrobial resistance (AMR) prediction
 in bacterial pathogens.
 
-<img src="man/figures/README-amR.png" width="60%" style="display: block; margin: auto;" />
+<img src="man/figures/README-amR.png" alt="" width="60%" style="display: block; margin: auto;" />
 
 ## The amR suite
 
@@ -23,7 +23,7 @@ The amR suite consists of three packages that work together:
 |----|----|----|
 | **amRdata** | Data curation and feature extraction from bacterial genomes | [JRaviLab/amRdata](https://github.com/JRaviLab/amRdata) |
 | **amRml** | Machine learning models for AMR prediction | [JRaviLab/amRml](https://github.com/JRaviLab/amRml) |
-| **amRshiny** | Interactive dashboard for exploring results | [JRaviLab/amRshiny](https://github.com/JRaviLab/amRshiny) |
+| **amRviz** | Interactive dashboard for exploring results | [JRaviLab/amRviz](https://github.com/JRaviLab/amRviz) |
 
 ## Installation
 
@@ -48,7 +48,7 @@ You can also install packages individually:
 ``` r
 remotes::install_github("JRaviLab/amRdata")
 remotes::install_github("JRaviLab/amRml")
-remotes::install_github("JRaviLab/amRshiny")
+remotes::install_github("JRaviLab/amRviz")
 ```
 
 ## Quick start
@@ -57,22 +57,22 @@ remotes::install_github("JRaviLab/amRshiny")
 # Load all packages
 library(amRdata)
 library(amRml)
-library(amRshiny)
+library(amRviz)
 
 # 1. Prepare data with amRdata
 # features <- prepareFeatures(...)
 
 # 2. Train ML models with amRml
 # results <- runMLPipeline(...)
 
-# 3. Explore results with amRshiny
+# 3. Explore results with amRviz
 # launchDashboard(...)
 ```
 
 ## Workflow overview
 
     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
-    │  amRdata    │ --> │   amRml     │ --> │  amRshiny   │
+    │  amRdata    │ --> │   amRml     │ --> │  amRviz   │
     │             │     │             │     │             │
     │ - Genomes   │     │ - Train LR  │     │ - Dashboard │
     │ - Features  │     │ - Evaluate  │     │ - Plots     │
@@ -83,7 +83,7 @@ library(amRshiny)
 
 - [amRdata documentation](https://jravilab.github.io/amRdata)
 - [amRml documentation](https://jravilab.github.io/amRml)
-- [amRshiny documentation](https://jravilab.github.io/amRshiny)
+- [amRviz documentation](https://jravilab.github.io/amRviz)
 
 ## Citation
 

diff --git a/amR.Rproj b/amR.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 6761cf5b-842c-4498-aaa3-1f6bf1b396a4
 
 RestoreWorkspace: No
 SaveWorkspace: No

diff --git a/man/figures/README-amR.png b/man/figures/README-amR.png
diff --git a/man/installAMR.Rd b/man/installAMR.Rd
diff --git a/tests/testthat/test-install.R b/tests/testthat/test-install.R
@@ -2,13 +2,13 @@ test_that("installAMR validates package arguments", {
   # Valid packages should not error
 
   expect_no_error(
-    match.arg(c("data", "ml"), c("data", "ml", "shiny"), several.ok = TRUE)
+    match.arg(c("data", "ml"), c("data", "ml", "viz"), several.ok = TRUE)
   )
 
   # Invalid package should error
 
   expect_error(
-    match.arg("invalid", c("data", "ml", "shiny"), several.ok = TRUE)
+    match.arg("invalid", c("data", "ml", "viz"), several.ok = TRUE)
   )
 })
 
@@ -28,7 +28,7 @@ test_that("installAMR has correct default arguments", {
   args <- formals(installAMR)
 
   # Check default values
-  expect_equal(eval(args$packages), c("data", "ml", "shiny"))
+  expect_equal(eval(args$packages), c("data", "ml", "viz"))
   expect_false(eval(args$force))
   expect_equal(eval(args$upgrade), "default")
   expect_false(eval(args$quiet))

diff --git a/vignettes/intro.Rmd b/vignettes/intro.Rmd
@@ -19,7 +19,7 @@ The amR package suite provides tools for antimicrobial resistance (AMR) predicti
 
 - **amRdata**: Data curation and feature extraction from bacterial genomes
 - **amRml**: Machine learning models for AMR prediction
-- **amRshiny**: Interactive dashboard for exploring results
+- **amRviz**: Interactive dashboard for exploring results
 
 ## Installation
 
@@ -30,7 +30,7 @@ library(amR)
 installAMR()
 ```
 
-This will install amRdata, amRml, and amRshiny from GitHub.
+This will install amRdata, amRml, and amRviz from GitHub.
 
 ### Installing specific packages
 
@@ -64,6 +64,6 @@ installAMR(upgrade = "never")
 A typical amR workflow involves:
 1. Preparing genomic feature data with **amRdata**
 2. Training ML models with **amRml**
-3. Exploring results with **amRshiny**
+3. Exploring results with **amRviz**
 
 See the individual package vignettes for detailed usage instructions.