text: user4612: Could you please suggest suitable R packages for statistical coupling and direct coupling analysis of protein sequences?
  I know there are Python packages for those: SCA and DCA.
  I was looking for similar packages in R so that I can continue with my present R workflow.
  
  user181: While my suggestion doesn't technically fit your qualifications, I do think it may be useful. I bring this up because you mention you want to continue with your R workflow. I am assuming you don't have anything against Python, but just want to stick with R to keep a tidy project. 
  The R package reticulate (homepage) allows for a very easy interface between Python and R. I use it to run the Python package scrublet to remove doublets from scRNA-Seq data before analysis with a popular R package. 
  Just for reference, here is what using a Python package in R might look like:
  ## setup
  Sys.setenv("RETICULATE_PYTHON"="/n/apps/CentOS7/install/pyenv-1.0.0/pyenv/shims/python3")
  use_python("/n/apps/CentOS7/install/pyenv-1.0.0/pyenv/shims/python3", required=TRUE)
  reticulate::py_config()
  scr <- import(module="scrublet", convert=FALSE)
  
  ## usage
  E150_scrub <- scr$Scrublet(r_to_py(seurat_object@assays$RNA@counts)$T$tocsc(), expected_doublet_rate=0.06)
  doublet_results <- E150_scrub$scrub_doublets(min_counts=2, min_cells=3, min_gene_variability_pctl=85)
  
  The Python package function calls look like scr$Scrublet(). There are some very convenient features like easy conversion of numpy tables to dataframes and the like. If you use RMarkdown, you can even display inline plots from matplotlib. I highly recommend giving it a try, it smoothly bridges the gap between the spheres of development of bioinformatic tools.
  
original_text: 4612: <p>Could you please suggest suitable R packages for statistical coupling and direct coupling analysis of protein sequences?<br>
  I know there are Python packages for those: <a href="https://reynoldsk.github.io/pySCA" rel="nofollow noreferrer" title="pySCA">SCA</a> and <a href="https://github.com/giopina/pydca" rel="nofollow noreferrer" title="pyDCA">DCA</a>.
  I was looking for similar packages in R so that I can continue with my present R workflow.</p>
   181: <p>While my suggestion doesn't technically fit your qualifications, I do think it may be useful. I bring this up because you mention you want to continue with your R workflow. I am assuming you don't have anything against Python, but just want to stick with R to keep a tidy project. </p>
  
  <p>The R package <code>reticulate</code> (<a href="https://rstudio.github.io/reticulate/" rel="nofollow noreferrer">homepage</a>) allows for a very easy interface between Python and R. I use it to run the Python package <code>scrublet</code> to remove doublets from scRNA-Seq data before analysis with a popular R package. </p>
  
  <p>Just for reference, here is what using a Python package in R might look like:</p>
  
  <pre><code>## setup
  Sys.setenv("RETICULATE_PYTHON"="/n/apps/CentOS7/install/pyenv-1.0.0/pyenv/shims/python3")
  use_python("/n/apps/CentOS7/install/pyenv-1.0.0/pyenv/shims/python3", required=TRUE)
  reticulate::py_config()
  scr &lt;- import(module="scrublet", convert=FALSE)
  
  ## usage
  E150_scrub &lt;- scr<span class="math-container">$Scrublet(r_to_py(seurat_object@assays$</span>RNA@counts)<span class="math-container">$T$</span>tocsc(), expected_doublet_rate=0.06)
  doublet_results &lt;- E150_scrub$scrub_doublets(min_counts=2, min_cells=3, min_gene_variability_pctl=85)
  </code></pre>
  
  <p>The Python package function calls look like <code>scr$Scrublet()</code>. There are some very convenient features like easy conversion of numpy tables to dataframes and the like. If you use RMarkdown, you can even display inline plots from <code>matplotlib</code>. I highly recommend giving it a try, it smoothly bridges the gap between the spheres of development of bioinformatic tools.</p>
  