[REVIEW]: RandomForestsGLS: An R package for Random Forests for dependent data

[whedon]

Submitting author: @ArkajyotiSaha (Arkajyoti Saha)
Repository: https://github.com/ArkajyotiSaha/RandomForestsGLS
Branch with paper.md (empty if default branch):
Version: v0.1.4.JOSS
Editor: @fabian-s
Reviewers: @mnwright, @pdwaggoner
Archive: 10.5281/zenodo.6257157

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Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of Need' that clearly states what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

Review checklist for @pdwaggoner

✨ Important: Please do not use the Convert to issue functionality when working through this checklist, instead, please open any new issues associated with your review in the software repository associated with the submission. ✨

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@ArkajyotiSaha) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of Need' that clearly states what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[whedon]

Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. @mnwright, @pdwaggoner it looks like you're currently assigned to review this paper 🎉.

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[whedon]

Wordcount for paper.md is 2766

[whedon]

Software report (experimental):

github.com/AlDanial/cloc v 1.88  T=0.03 s (594.6 files/s, 87811.0 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
C++                              2            304             55            980
R                               11             92             42            547
Markdown                         2             76              0            142
Rmd                              1            102            143            137
TeX                              1             11              0            108
C                                1              4              4             20
C/C++ Header                     1             11             12             16
-------------------------------------------------------------------------------
SUM:                            19            600            256           1950
-------------------------------------------------------------------------------


Statistical information for the repository '08378bffd337f88f29666b71' was
gathered on 2021/09/29.
The following historical commit information, by author, was found:

Author                     Commits    Insertions      Deletions    % of changes
Arkajyoti Saha                   4          1837            431          100.00

Below are the number of rows from each author that have survived and are still
intact in the current revision:

Author                     Rows      Stability          Age       % in comments
Arkajyoti Saha             1406           76.5          0.6                5.12

[whedon]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1080/01621459.2021.1950003 is OK
- 10.1007/bf00058655 is OK
- 10.1023/A:1010933404324 is OK
-  10.7717/peerj.5518 is OK
- 10.1080/10106049.2019.1595177 is OK
- 10.1016/j.najef.2018.06.013 is OK
- 10.1080/01621459.2015.1044091 is OK
- 10.1109/99.660313 is OK
- 10.1201/9781315139470 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[whedon]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[fabian-s]

👋🏼 @ArkajyotiSaha @mnwright @pdwaggoner

this is the review thread for the paper. All of our communications will happen here from now on.

Both reviewers have checklists at the top of this thread with the JOSS requirements. As you go over the submission, please check any items that you feel have been satisfied. There are also links to the JOSS reviewer guidelines.

The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, the reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention openjournals/joss-reviews#REVIEW_NUMBER so that a link is created to this thread (and I can keep an eye on what is happening). Please also feel free to comment and ask questions on this thread. In my experience, it is better to post comments/questions/suggestions as you come across them instead of waiting until you've reviewed the entire package.

We aim for reviews to be completed within about 2-4 weeks (Marvin already told me he might need a little bit more time, that's fine). Please let me know if you expect additional delays.
We can also use Whedon (our bot) to set automatic reminders if you know you'll be away for a known period of time.

Please feel free to ping me (@fabian-s) if you have any questions/concerns.

[pdwaggoner]

@ArkajyotiSaha @fabian-s et al. - Overall, this package is great. A useful extension of RF, and a great complement to the paper introducing the method. My feedback is mostly focused on high level items and involves fixes to ease consumption of the paper and code, and thus application and interpretation. No PRs as nothing major needed to be changed, by me at least. I hope there are some useful comments here for the authors. Thanks!

Re: the code, how is optimization defined when param_estimate = TRUE in the context of unknown covariance parameters? More defining and defending this (ideally in the paper and code/documentation) would be useful.

Re: the code, and specifically this criterion from JOSS: “A summary describing the high-level functionality and purpose of the software for a diverse, non-specialist audience.”, the summary (and statement of need by extension) don’t fully meet this standard. The language does a job focusing on the computational benefits of RandomForestsGLS, as well as the value in a statistical sense. But the functionality and focus of the package (rather than the method), is lacking. The details and value of the method, though needed at a high level to understand the package, are fully unpacked in the saha2021random paper. So, I wanted much more focus on introducing and convincing a non-specialist, skeptical audience of the need and value of this software tool. To be sure, the details of the package construction and design are well-discussed. But the implementation of the package, and how it might be tied into the modal ML workflow, for example, are missing. Of note: Once addressed, I will check off the related item in the review form. Ping me (@pdwaggoner) once addressed so I can complete the review form.

Why only choose autoregression for the time series dependency? As with any method, there are several assumptions with this approach/method (namely, assuming autoregressive errors). Its definitely widely used and AR is often the most common type of history dependence, and thus a good starting place. But I’d recommend, perhaps even for later package versions, other time series methods to be included in this framework, both parametric and nonparametric (e.g., ECM, ARFIMA, random walk, and so on).

Re: the paper, there were many grammatical issues throughout (e.g., "felicitates” in the Statement of Need), as well as informal syntax (contractions like “doesn't” used throughout). I recommend cleaning up and revising the manuscript several times across several readers. These types of mistakes are a bit distracting. Of note: Once addressed, I will check off the related item in the review form. Ping me (@pdwaggoner) once addressed so I can complete the review form.

Re: the paper, I wanted to see a more explicit and clearer definition of the core concept, “dependency” up front. It is mentioned a lot throughout and in the title. The authors do a good job of relating the similarity of OLS -> GLS, for the current move from RF -> RF-GLS. And there is a reference to “spatial and temporal correlation” in the Summary. But other than this, I was a bit confused and often left wondering about the many other contexts, definitions and cases that “dependency” could mean. So a crisper set up and definition for such a central concept would really benefit the paper and help situate the reader right off the bat.

Though in the vignette, I don’t get the purpose of the following in the RFGLS_estimate_timeseries.Rd manual page:

rmvn <- function(n, mu = 0, V = matrix(1)){
  p <- length(mu)
  if(any(is.na(match(dim(V),p))))
    stop("Dimension not right!")
  D <- chol(V)
  t(matrix(rnorm(n*p), ncol=p)\%*\%D + rep(mu,rep(n,p)))
}

I couldn’t see anywhere rmvn was called. Could’ve missed something.

I could imagine core functions (e.g., RFGLS_estimate_spatial) being slow with big data sets. On replicating some of the parts of the vignette, it was pretty fast. But perhaps wrapping computation in a progress bar would be a nice UI addition. For example, something like:

RFGLS_estimate_spatial <- function(coords, y, X, Xtest = NULL, nrnodes = NULL, nthsize = 20, mtry = 1, pinv_choice = 1, n_omp = 1, ntree = 50, h = 1,
                                   sigma.sq = 1, tau.sq = 0.1, phi = 5, nu = 0.5, n.neighbors = 15, cov.model = "exponential", search.type = "tree",
                                   param_estimate = FALSE, verbose = FALSE){

progressr::with_progress( # start progress bar here via `progressr`

  n <- nrow(coords)
  nsample <- n
  if(is.null(nrnodes)){
    nrnodes <- 2 * nsample + 1
  }

  if(is.null(Xtest)){
    Xtest <- X
  }
  if(ncol(Xtest) != ncol(X)){ stop(paste("error: Xtest must have ",ncol(X)," columns\n"))}

  if(param_estimate){
    sp <- randomForest(X, y, nodesize = nthsize)
    sp_input_est <- predict(sp, X)
    rf_residual <- y - sp_input_est
    if(verbose){
      cat(paste(("----------------------------------------"), collapse="   "), "\n"); cat(paste(("\tParameter Estimation"), collapse="   "), "\n"); cat(paste(("----------------------------------------"), collapse="   "), "\n")
    }
    est_theta <- BRISC_estimation(coords, x = matrix(1,n,1), y = rf_residual, verbose = verbose, cov.model = cov.model)
    sigma.sq <- est_theta$Theta[1]
    tau.sq <- est_theta$Theta[2]
    phi <- est_theta$Theta[3]
    if(cov.model =="matern"){
      nu <- est_theta$Theta[4]
    }
  }

  cov.model.names <- c("exponential","spherical","matern","gaussian")
  cov.model.indx <- which(cov.model == cov.model.names) - 1
  storage.mode(cov.model.indx) <- "integer"

  ##Parameter values
  if(cov.model!="matern"){
    initiate <- c(sigma.sq, tau.sq, phi)
    names(initiate) <- c("sigma.sq", "tau.sq", "phi")
  }
  else{
    initiate <- c(sigma.sq, tau.sq, phi, nu)
    names(initiate) <- c("sigma.sq", "tau.sq", "phi", "nu")}

  alpha.sq.starting <- sqrt(tau.sq/sigma.sq)
  phi.starting <- sqrt(phi)
  nu.starting <- sqrt(nu)

  storage.mode(alpha.sq.starting) <- "double"
  storage.mode(phi.starting) <- "double"
  storage.mode(nu.starting) <- "double"

  search.type.names <- c("brute", "tree")
  if(!search.type %in% search.type.names){
    stop("error: specified search.type '",search.type,"' is not a valid option; choose from ", paste(search.type.names, collapse=", ", sep="") ,".")
  }
  search.type.indx <- which(search.type == search.type.names)-1
  storage.mode(search.type.indx) <- "integer"


  ##Option for Multithreading if compiled with OpenMp support
  n.omp.threads <- as.integer(n_omp)
  storage.mode(n.omp.threads) <- "integer"

  fix_nugget <- 1
  ##type conversion
  storage.mode(n) <- "integer"
  storage.mode(coords) <- "double"
  storage.mode(n.neighbors) <- "integer"
  storage.mode(verbose) <- "integer"

  if(verbose){
    cat(paste(("----------------------------------------"), collapse="   "), "\n"); cat(paste(("\tRFGLS Model Fitting"), collapse="   "), "\n"); cat(paste(("----------------------------------------"), collapse="   "), "\n")
  }

  res_BF <- .Call("RFGLS_BFcpp", n, n.neighbors, coords, cov.model.indx, alpha.sq.starting, phi.starting, nu.starting, search.type.indx, n.omp.threads, verbose, PACKAGE = "RandomForestsGLS")
  res_Z <- .Call("RFGLS_invZcpp", as.integer(length(res_BF$nnIndxLU)/2), as.integer(res_BF$nnIndx), as.integer(res_BF$nnIndxLU), as.integer(rep(0, length(res_BF$nnIndxLU)/2)), as.integer(0*res_BF$nnIndx), as.integer(rep(0, length(res_BF$nnIndxLU)/2 + 1)), as.integer(rep(0, length(res_BF$nnIndxLU)/2)), PACKAGE = "RandomForestsGLS")

  p <- ncol(X)
  storage.mode(p) <- "integer"
  storage.mode(nsample) <- "integer"

  storage.mode(nthsize) <- "integer"
  if(is.null(nrnodes)){
    nrnodes <- 2 * nsample + 1
  }
  storage.mode(nrnodes) <- "integer"

  storage.mode(mtry) <- "integer"
  treeSize <- 0
  storage.mode(treeSize) <- "integer"

  storage.mode(pinv_choice) <- "integer"

  ntest <- nrow(Xtest)
  storage.mode(ntest) <- "integer"
  if(is.null(h)){h <- 1}

  q <- 0
  storage.mode(q) <- "integer"

  local_seed <- sample(.Random.seed, 1)


  if(h > 1){
    cl <- makeCluster(h)
    clusterExport(cl=cl, varlist=c("X", "y", "res_BF", "res_Z", "mtry", "n", "p",
                                   "nsample", "nthsize", "nrnodes", "treeSize", "pinv_choice", "Xtest", "ntest",
                                   "n.omp.threads", "RFGLS_tree", "q", "local_seed"),envir=environment())
    if(verbose == TRUE){
      cat(paste(("----------------------------------------"), collapse="   "), "\n"); cat(paste(("\tRF Progress"), collapse="   "), "\n"); cat(paste(("----------------------------------------"), collapse="   "), "\n")
      pboptions(type = "txt", char = "=")
      result <- pblapply(1:ntree,RFGLS_tree, X, y, res_BF, res_Z, mtry, n, p,
                         nsample, nthsize, nrnodes, treeSize, pinv_choice, Xtest, ntest,
                         n.omp.threads, q, local_seed, cl = cl)
    }
    if(verbose != TRUE){result <- parLapply(cl,1:ntree,RFGLS_tree, X, y, res_BF, res_Z, mtry, n, p,
                                            nsample, nthsize, nrnodes, treeSize, pinv_choice, Xtest, ntest,
                                            n.omp.threads, q, local_seed)}
    stopCluster(cl)
  }
  if(h == 1){
    if(verbose == TRUE){
      cat(paste(("----------------------------------------"), collapse="   "), "\n"); cat(paste(("\tRF Progress"), collapse="   "), "\n"); cat(paste(("----------------------------------------"), collapse="   "), "\n")
      pboptions(type = "txt", char = "=")
      result <- pblapply(1:ntree,RFGLS_tree, X, y, res_BF, res_Z, mtry, n, p,
                         nsample, nthsize, nrnodes, treeSize, pinv_choice, Xtest, ntest,
                         n.omp.threads, q, local_seed)
    }

    if(verbose != TRUE){
      result <- lapply(1:ntree,RFGLS_tree, X, y, res_BF, res_Z, mtry, n, p,
                       nsample, nthsize, nrnodes, treeSize, pinv_choice, Xtest, ntest,
                       n.omp.threads, q, local_seed)
    }
  }

  RFGLS_out <- list()
  RFGLS_out$P_matrix <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$P_index))
  RFGLS_out$predicted_matrix <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$ytest))
  RFGLS_out$predicted <- rowMeans(RFGLS_out$predicted_matrix)
  RFGLS_out$X <- X
  RFGLS_out$y <- y
  RFGLS_out$coords <- coords
  RFGLS_out$RFGLS_object <- list()
  RFGLS_out$RFGLS_object$ldaughter <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$lDaughter))
  RFGLS_out$RFGLS_object$rdaughter <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$rDaughter))
  RFGLS_out$RFGLS_object$nodestatus <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$nodestatus))
  RFGLS_out$RFGLS_object$upper <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$upper))
  RFGLS_out$RFGLS_object$avnode <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$avnode))
  RFGLS_out$RFGLS_object$mbest <- do.call(cbind, lapply(1:ntree, function(i) result[[i]]$mbest))

) # close progress bar here

  return(RFGLS_out)
}

If you like this, happy to open a PR and drop it in the functions for each if it would help. Let me know.

[whedon]

👋 @mnwright, please update us on how your review is going (this is an automated reminder).

[whedon]

👋 @pdwaggoner, please update us on how your review is going (this is an automated reminder).

[pdwaggoner]

Finished mine a while ago (14 days). See above in this thread

Ping @ArkajyotiSaha and @fabian-s

[fabian-s]

@ArkajyotiSaha
while we wait for @mnwright to start their review, please adress @pdwaggoner points/questions/remarks from their comment above?

[ArkajyotiSaha]

Sounds great! I am working on addressing @pdwaggoner comments, will let @fabian-s and @pdwaggoner know, once I am done with them!

[mnwright]

I think this is a very useful extension of random forests and a promising package. The examples where the methods outperforms standard RF are quite impressing! I have a few general questions, some on the package and some on the paper:

General

• From what I understand, there are two major differences to standard RF: The bootstrap procedure and the splitting rule. Why not take an existing RF package such as randomForest or ranger and make these changes instead of setting up a new package "borrowing some code from randomForest"?
• Fitting RF-GLS is slower than standard RF. How much is it slower? How does it scale with the number of observations, covariates or other data or model parameters?
• Is a real data example available? That would be of interest for the method itself (not the focus here) but also for the package to see how it scales and for which real data purpose it can be used.

Package

• The C++ code is not documented/commented well and hard to understand.
• The DESCRIPTIONS still contains a link to arxiv, not the published paper.
• The README is missing a link to the paper.
• Typo in README: criterion.
• Tests just run examples and check output types/sizes. That could be improved with more tests and tests that check for correct output.
• Is any kind of continuous integration used? I think it is useful to at least run the tests with each commit/PR.
• Maybe too late to change that, but I think the package name is not a great choice. For example, at first try, I typed "randomForestGLS", then capitalized to "RandomForestGLS" and finally corrected to "RandomForestsGLS". It's also quite long and you have to remember the capitalization.

Paper:

• The JASA paper is called "Random Forests for Spatially Dependent Data", the software has the same name without "Spatially". Are additional types of dependencies covered by the software, not described in the original JASA paper? If yes, please detail in the JOSS paper.
• line 8: Should be "in these models"
• lines 14-15: "hence is not optimal in mixed-model approach". I don't understand this. Wouldn't RF be used as an alternative to the mixed model and not IN the mixed model approach?
• lines 18-19: Avoid linebreak in package name
• line 63: for or model correlation?
• line 176: optimizing a cost function (missing a)
• line 211: of the RF-GLS method (missing the)
• line 216: "Efficient implementation thorough" should be through?
• line 217: Maybe remove "clever"?
• References: Datta et al. is in title case, others in sentence case.
• In general, many spelling errors, missing articles, etc.

[fabian-s]

@ArkajyotiSaha what's your timeline for adressing our reviewers' comments?

[ArkajyotiSaha]

@fabian-s I am working on the revision and am almost done with them. I plan to submit them by the end of the thanksgiving weekend (29th Nov). Please let me know if the timeline works for you. Thanks!

[fabian-s]

great, thanks for the update.

[ArkajyotiSaha]

@fabian-s , @pdwaggoner @mnwright We thank the Editor and the reviewers for their positive feedback and thoughtful comments which have helped to improve the manuscript. We have tried to address all the reviewer comments in the software and the paper. Updated versions of the package and the paper are available in the associated GitHub repository (https://github.com/ArkajyotiSaha/RandomForestsGLS).
A detailed point-by-point response letter is available in https://github.com/ArkajyotiSaha/RandomForestsGLS/blob/main/JOSS_authors_response_letter.pdf . The response letter is divided in two sections, with each section addressing the comments of one of the reviewers (Section 1: @pdwaggoner ; Section 2: @mnwright).
Please let me know if I can provide any additional information. Thanks for your time and consideration!

[fabian-s]

@whedon generate pdf

[whedon]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[pdwaggoner]

Satisfied. Well done!

[fabian-s]

@mnwright please let us know if you see any remaining points that need to be adressed.

[mnwright]

Thanks for the extensive revision. It looks fine except one thing: I still cannot find the comments in the .cpp files.

[editorialbot]

⚠️ Error prepararing acceptance. The generated XML metadata file is invalid.

[arfon]

@xuanxu looks like the DOI 10.7717/peerj.5518 is causing the XML validation to fail here (although it's clearly a valid DOI). Perhaps we shouldn't fail the workflow here?

@fabian-s @ArkajyotiSaha, apologies for the noise here. We switched over to the new bot infrastructure yesterday and you're helping us find a few bugs 😅

[fabian-s]

no worries - anything I can do to help?

[arfon]

no worries - anything I can do to help?

Nope, I think we're good for now.

[tarleb]

The problem was that the DOI has a leading space in the bib file. Our pipeline should be able to auto-correct minor issues like that. I've pushed a change that should allow validation to succeed now.

[editorialbot]

👋 @openjournals/joss-eics, this paper is ready to be accepted and published.

Check final proof 👉 openjournals/joss-papers#2999

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#2999, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

[fabian-s]

congratulations, @ArkajyotiSaha!

[ArkajyotiSaha]

@fabian-s Thanks so much! Please let me know if there is anything on my end to be taken care of!

[arfon]

@editorialbot accept

[editorialbot]

Doing it live! Attempting automated processing of paper acceptance...

[editorialbot]

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.03780 joss-papers#3000
1. Wait a couple of minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.03780
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

[arfon]

@mnwright, @pdwaggoner – many thanks for your reviews here and to @fabian-s for editing this submission! JOSS relies upon the volunteer effort of people like you and we simply wouldn't be able to do this without you ✨

@ArkajyotiSaha – your paper is now accepted and published in JOSS ⚡🚀💥

[editorialbot]

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

If you would like to include a link to your paper from your README use the following code snippets:

Markdown:
[![DOI](https://joss.theoj.org/papers/10.21105/joss.03780/status.svg)](https://doi.org/10.21105/joss.03780)

HTML:
<a style="border-width:0" href="https://doi.org/10.21105/joss.03780">
  <img src="https://joss.theoj.org/papers/10.21105/joss.03780/status.svg" alt="DOI badge" >
</a>

reStructuredText:
.. image:: https://joss.theoj.org/papers/10.21105/joss.03780/status.svg
   :target: https://doi.org/10.21105/joss.03780

This is how it will look in your documentation:

We need your help!

The Journal of Open Source Software is a community-run journal and relies upon volunteer effort. If you'd like to support us please consider doing either one (or both) of the the following:

• Volunteering to review for us sometime in the future. You can add your name to the reviewer list here: https://joss.theoj.org/reviewer-signup.html
• Making a small donation to support our running costs here: https://numfocus.org/donate-to-joss

[ArkajyotiSaha]

Hi @fabian-s I just noticed that the link to the software archive at https://joss.theoj.org/papers/10.21105/joss.03780 is not working. This directs to 10.21105/zenodo.6257157 instead of the original zenodo archive at 10.5281/zenodo.6257157 . I was wondering if there is a way to fix this. Thanks!

[kyleniemeyer]

Ah, that appears to be some typo in the archive. @arfon can this be corrected after the fact?

[arfon]

@editorialbot set 10.5281/zenodo.6257157 as archive

[editorialbot]

Done! Archive is now 10.5281/zenodo.6257157

[arfon]

@editorialbot accept

[editorialbot]

Doing it live! Attempting automated processing of paper acceptance...

[editorialbot]

🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦

[editorialbot]

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.03780 joss-papers#3054
1. Wait a couple of minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.03780
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...