update github action

.github/workflows/R-CMD-check.yaml

@@ -1,5 +1,4 @@
-# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
-# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+# Workflow derived from [https://github.com/r-lib/actions/tree/v2/examples](https://github.com/r-lib/actions/tree/v2/examples)
 on:
   push:
     branches: [main, master]
@@ -24,7 +23,6 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
 
-
     steps:
       - uses: actions/checkout@v3
 
@@ -39,11 +37,12 @@ jobs:
       - name: Install R dependencies
         uses: r-lib/actions/setup-r-dependencies@v2
         with:
-            extra-packages: rcmdcheck, keras3  # Add keras to the list of extra packages
+          extra-packages: any::rcmdcheck
+          needs: check
 
-      - name: Install Keras Python dependencies
-        run: R -e 'keras3::install_keras()'  # This runs the install_keras function from R
-
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true
+          # If the vignette continues to hang, uncomment the lines below to skip it temporarily
+          # args: 'c("--no-vignettes", "--no-build-vignettes")'
+          # build_args: 'c("--no-build-vignettes")'

.github/workflows/test-coverage.yaml

@@ -25,12 +25,9 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::covr, any::xml2, rcmdcheck, keras3 
+          extra-packages: any::covr, any::xml2, rcmdcheck
           needs: coverage
 
-      - name: Install Keras Python dependencies
-        run: R -e 'keras3::install_keras()'  # This runs the install_keras function from R
-
       - name: Test coverage
         run: |
           cov <- covr::package_coverage(

DESCRIPTION

@@ -38,7 +38,6 @@ Suggests:
         testthat,
 	tidygraph,
 	viridis
-SystemRequirements: Python (via basilisk)
 LinkingTo:
 	Rcpp
 VignetteBuilder: knitr

vignettes/immApex.Rmd

@@ -58,11 +58,11 @@ Parameters for ```getIMGT()```
 
 Here, we will use the ```getIMGT()``` function to get the amino acid sequences for the TRBV region to get all the sequences by V gene allele.
 
-```{r, eval=knitr::is_html_output()}
+```{r}
 # Function to check IMGT website availability
 is_imgt_available <- function() {
   tryCatch({
-    r <- httr::HEAD("https://www.imgt.org", timeout(5))
+    r <- httr::HEAD("https://www.imgt.org", httr::timeout(5))
     httr::status_code(r) == 200
   }, error = function(e) {
     FALSE
@@ -121,7 +121,7 @@ Parameters for ```inferCDR```
 * **sequence.type** Type of sequence - "aa" for amino acid or "nt" for nucleotide
 * **sequences** The specific regions of the CDR loop to get from the data.
 
-```{r }
+```{r}
 if (is_imgt_available()) {
   Adaptive_example <- inferCDR(Adaptive_example,
                                chain = "TRB", 
@@ -155,7 +155,7 @@ Parameters for ```generateSequences()```
 ```{r }
 sequences <- generateSequences(prefix.motif = "CAS",
                                suffix.motif = "YF",
-                               number.of.sequences = 1000,
+                               number.of.sequences = 200,
                                min.length = 8,
                                max.length = 16)
 sequences <- unique(sequences)
@@ -165,7 +165,7 @@ head(sequences)
 If we want to generate nucleotide sequences instead of amino acids, we must to change the **sequence.dictionary**.
 
 ```{r }
-nucleotide.sequences <- generateSequences(number.of.sequences = 1000,
+nucleotide.sequences <- generateSequences(number.of.sequences = 200,
                                           min.length = 8,
                                           max.length = 16, 
                                           sequence.dictionary = c("A", "C", "T", "G"))
@@ -575,15 +575,15 @@ First, we'll simulate two distinct classes of sequences using ```generateSequenc
 # Step 1a: Generate two distinct classes of sequences
 class1.sequences <- generateSequences(prefix.motif = "CAS",
                                       min.length = 3,
-                                      number.of.sequences = 500)
+                                      number.of.sequences = 250)
 
 class2.sequences <- generateSequences(prefix.motif = "CSG",
                                       min.length = 3,
-                                      number.of.sequences = 500)
+                                      number.of.sequences = 250)
 
 # Combine sequences and create labels
 all.sequences <- c(class1.sequences, class2.sequences)
-labels <- as.factor(c(rep("Class1", 500), rep("Class2", 500)))
+labels <- as.factor(c(rep("Class1", 250), rep("Class2", 250)))
 
 # Step 1b: Use propertyEncoder to create a feature matrix from Atchley factors
 feature.matrix <- propertyEncoder(all.sequences,