[REVIEW]: dsmmR: Estimation and Simulation of Drifting Semi-Markov Models

editorialbot commented 1 year ago

Submitting author: !--author-handle-->@Mavrogiannis-Ioannis@Bisaloo<!--end-editor-- Reviewers: @Athene-ai, @RomainAzais, @strevezas Archive: 10.5281/zenodo.13117522

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Reviewer instructions & questions

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✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @Athene-ai

📝 Checklist for @RomainAzais

📝 Checklist for @strevezas

editorialbot commented 1 year ago

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editorialbot commented 1 year ago

Software report:

github.com/AlDanial/cloc v 1.88  T=0.02 s (864.6 files/s, 308903.6 lines/s)
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Language                     files          blank        comment           code
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R                                8             87           2752           1878
Markdown                         5            232              0            751
TeX                              1             19              0             90
YAML                             2             12              6             56
Rmd                              1             49            121             21
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SUM:                            17            399           2879           2796
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gitinspector failed to run statistical information for the repository

editorialbot commented 1 year ago

Wordcount for paper.md is 901

editorialbot commented 1 year ago

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

OK DOIs

- 10.2202/1544-6115.1326 is OK
- 10.1007/s11009-018-9682-8 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot commented 1 year ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

Bisaloo commented 1 year ago

:wave: :wave: :wave: @Mavrogiannis-Ioannis @Athene-ai @RomainAzais this is the review thread for the paper. All of our communications will happen here from now on.

As a reviewer, the first step is to create a checklist for your review by entering

@editorialbot generate my checklist

as the top of a new comment in this thread.

These checklists contain the JOSS requirements. As you go over the submission, please check any items that you feel have been satisfied. The first comment in this thread also contains 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#5572 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. However, I have noted that @RomainAzais is not available to start the review before ~mid-July. We can also use EditorialBot (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 (@bisaloo) if you have any questions/concerns.

Athene-ai commented 1 year ago

Review checklist for @Athene-ai

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

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/Mavrogiannis-Ioannis/dsmmR?
[x] 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 (@Mavrogiannis-Ioannis) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[x] Installation: Does installation proceed as outlined in the documentation?
[ ] Functionality: Have the functional claims of the software been confirmed?
[x] 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

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

[x] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[x] 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, who the target audience is, and its relation to other work?
[x] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[x] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[x] 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?

Mavrogiannis-Ioannis commented 1 year ago

Looking forward working with you @Athene-ai @RomainAzais to bring the paper to fruition! Also thank you @Bisaloo for your consistent work and searching. Ioannis

RomainAzais commented 1 year ago

Review checklist for @RomainAzais

Conflict of interest

[x] 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

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/Mavrogiannis-Ioannis/dsmmR?
[x] License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@Mavrogiannis-Ioannis) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[x] 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

[x] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
[x] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
[x] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
[x] 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?
[x] 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

[x] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[x] 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, who the target audience is, and its relation to other work?
[x] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[x] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[x] 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?

Mavrogiannis-Ioannis commented 1 year ago

Hello and good morning everyone, Yesterday I made a small update to the github page of the package, specifically: adding an Acknowledgements section in the README and DESCRIPTION files, regarding the financing that made the package possible.

It does not affect the paper or the code in any way. In fact, the financing was already mentioned in the paper.md file that you are reviewing. I hope you enjoyed your "vacations"! Hoping to hear from you, soon.

Ioannis

Bisaloo commented 1 year ago

Hi @RomainAzais :wave:, are you able to provide a timeline for when you will be able to complete your review? Thanks a lot for your help!

RomainAzais commented 1 year ago

Hello, Sorry for the delay. I am working on it. I think it will be done for the first half of October. Best, Romain

RomainAzais commented 1 year ago

Hello @Mavrogiannis-Ioannis

I have a few first questions about the package and, more specifically, about model simulation.

If I understand correctly, the package only allows you to simulate model 3 where only the sojourn times drift. Why this restriction?

Beyond that, the output of the simulation function is the sequence of states, without their sojourn times. Don't you think this limits the package's interest in simulation? I also find the documentation a little ambiguous on this point.

Thank you for your feedback on these points.

Mavrogiannis-Ioannis commented 1 year ago

Hello @RomainAzais!

Thank you kindly for your feedback. I will do my best to understand your points and explain my point of view.

a. "If I understand correctly, the package only allows you to simulate model 3 where only the sojourn times drift. Why this restriction?"

The package allows the user to simulate a sequence from any given model. This can be model 1, 2 or 3. This can be seen in the Arguments section of simulate.dsmm(), where obj inherits from all possible objects, namely dsmm and one of: dsmm_fit_nonparametric, dsmm_nonparametric, dsmm_fit_parametric or dsmm_parametric.

Since these objects can have any model (seen in earlier documentation of functions fit_dsmm(), parametric_dsmm() and nonparametric_dsmm()) then we can have any model for the simulate function.

For example, you can run the Examples where you simply switch f_is_drifting and p_is_drifting to obtain Model 1 and 2.

Perhaps your initial concern lies in the Examples section of the function, where we only show an example for the 3rd model.

We chose this way of representation to showcase the functionality of simulate.dsmm(): you can give it any model you previously had (in the example's case obj_model_3) and it's easy to simulate from that point.

b. "Beyond that, the output of the simulation function is the sequence of states, without their sojourn times. Don't you think this limits the package's interest in simulation?...

That is true, the output value is just the "regular" sequence of states. This way the researcher can do as they wish with the sequence. They can obtain the embedded Markov chain and the corresponding sojourn times by running the function base::rle(). From this point of view, it is a simple matter to add to the output of simulate.dsmm(), I can do so at the weekend. Do you consider it necessary?

c. "... I also find the documentation a little ambiguous on this point."

Can you specify exactly which part?

Again, thank you a lot for your feedback. Hope to hear from you soon, Ioannis

RomainAzais commented 1 year ago

Hello @Mavrogiannis-Ioannis

In fact, I hadn't quite understood. Thanks for your reply: it's much clearer now.

Adding to the documentation that base::rle() can be used to obtain the embedded chain and sojourn times is a good idea indeed.

One might also expect simulate.dsmm() to return an object of the S3 class (keeping the parameters used for simulation). I don't know if it's a good idea, but any thoughts on this?

Thank you again for your explanations. Romain

Mavrogiannis-Ioannis commented 1 year ago

Hello @RomainAzais , thank you again for your insightful comments.

It is indeed intriguing for a dsmm_simulate object to exist, inheriting parameters from the object given in the simulate.dsmm() function; this way it will be easier to track down different simulated sequences, making it easy to use for researchers. Added documentation for the base::rle() function would indeed be useful for everyone using the software as well.

I will think about how to formulate this new object (so that it fits in to the other objects in the dsmm "family") and hopefully present it here in the week-end.

Automated tests will also be needed for this new object, similarly to all the other objects in the package. This will take perhaps longer to make, to make sure it's extensive. This will be done so that people can create their own dsmm_simulate object (although it's always easier to use the simulate.dsmm() function).

Thank you again for your feedback! Ioannis

Mavrogiannis-Ioannis commented 1 year ago

So, after a long consideration, I propose the following:

I will add in detailed example usage for base::rle() function in the #Examples section of both simulate.dsmm() AND fit_dsmm(). This way, I believe it will be clear to the users about the difference between a regular sequence (example: AAACTGGT), the embedded Markov chain (for the example: ACTGT) and the corresponding sojourn times (for the example: 31121).
Nothing else will change for the simulate.dsmm() function, aside from a more clear description of the returned object.
An important (yet subtle) change will be made in the fit_dsmm() function, following the same direction: change the returned object's seq attribute to emc.

I believe this is a good way to achieve the most clarity, while keeping the objects simple, without repeating too much of the same information across objects and variables. The user should be able to keep track of the models and sequences they are using. They only have to properly name their own variables.

Please, let me know your thoughts on this approach.

Thank you for your time and your patience, Ioannis

Mavrogiannis-Ioannis commented 1 year ago

Hello, the changes as explained on my previous comment are live on github : https://github.com/Mavrogiannis-Ioannis/dsmmR/tree/master

Thank you everyone for your work so far. I'm waiting for more of your comments. Best, Ioannis

RomainAzais commented 1 year ago

Hello @Mavrogiannis-Ioannis

Thanks for the food for thought and the changes. I tested the new version and read the explanations behind it: I think it is clear and easy to use now.

Regarding the package's functionalities, I think it would be worth clarifying that estimation requires only one trajectory observation (in long time) and not several trajectories (both frameworks being allowed in statistics of processes).

On this aspect, the nsim input variable of the simulate.dsmm() function can be confusing (especially with the other optional variable seq_length): maybe prefer names like horizon and max_horizon? (These are really minor points.)

Mavrogiannis-Ioannis commented 1 year ago

Dear @RomainAzais,

First of all, I just discussed with my one of my supervisors during the dsmmR package development, Nicolas Vergne, and we are going to add multiple trajectories as a possibility for the user as well. So I will make this part more well defined when I write the code (in 1-2 weekends...).

As for the simulate.dsmm(), I agree that it requires to spend some time to understand the seq_length attribute etc.

I want to ask you, are names like horizon and max_horizon well defined in the literature? If so, is it possible for you to refer to me an example usage of these terms?

Thank you very much for your support & patience.

Best regards, Ioannis

RomainAzais commented 1 year ago

When simulating a stochastic process (X_t) over a time window [0,T], T is often referred to as the horizon. This may be particularly true in the context of stochastic algorithms or optimal control. See, for example, these lecture notes on Markov decision processes.

max_horizon could denote an upper-bound of the horizon: if I remember correctly, if nsim is greater than seq_length, the simulation stops at seq_length.

By the way, it is great to be able to use multiple trajectories for estimation!

Best, Romain

Mavrogiannis-Ioannis commented 11 months ago

Hello @RomainAzais , thank you again for providing references for your previous comment, it is quite interesting!

After pondering about it, I think the best course of action is as follows, regarding the attributes nsim and seq_length of the simulate.dsmm() function:

nsim : Name stays the same, since it also is "inherited" from the generic simulate() function used in base R and users should be familiar with it.
- Also a comment for us to think about the possibility of the horizon and max_horizon naming for nsim and seq_length:
- For our stochastic model (X_t), that acts behind the simulate function, we will have t ranging in [0, T] but notice here that T is the model_size that is mentioned before. Notice also that nsim has a maximum value of model_size. This means that here we are moving alongside the Embedded Markov Chain (EMC).
- The resulting sequence from the simulate.dsmm() function, will be the combination of the EMC plus the Sojourn Times of each state; therefore, seq_length is moving alongside a different "scale" than nsim. So although it is interesting to propose horizon and max_horizon, they are moving alongside different time scales, perhaps it is not appropriate? This is just for us to think about, and also I'm not sure if I understand clearly the horizon/max horizon cases. So perhaps we can stick to the simple case.
seq_length : Name changes to max_seq_length. I think this name is easier to understand, as one can literally translate it to maximum sequence length, i.e. a limiting value for the length of the resulting sequence (both the EMC and the Soj. Times).

I am going to work on the multiple sequences (that I mentioned before), in the next weeks. I'm sorry for taking this long to reply, I had a lot of work for my PhD, a lot of travelling.

Also, I want to thank all of you, @Bisaloo @RomainAzais @Athene-ai for your contribution to this work in the past 6 months. Warm wishes to all of you for the holy season!

Best regards, Ioannis

Bisaloo commented 10 months ago

Hi all, thanks for you work so far and best wishes for this new year!

I'm adding a third reviewer to this review to ensure all relevant aspects of the submission are covered. Thanks!

Bisaloo commented 10 months ago

@editorialbot add @strevezas as reviewer

editorialbot commented 10 months ago

@strevezas added to the reviewers list!

Bisaloo commented 9 months ago

Hi all :wave:, let's try to move this submission forward:

@strevezas, are you able to provide a timeline for your review?
@Mavrogiannis-Ioannis, you mentioned in https://github.com/openjournals/joss-reviews/issues/5572#issuecomment-1825680051 you wanted to implement simulation of multiple trajectories. Do you have a timeline for this new feature?

Thanks a lot to everyone for your continued work on this submission. I understand it overlaps with other commitments but in my experience, it's good to try and get it out the door so you can remove it from your mind once and for all and move on :relaxed:.

Mavrogiannis-Ioannis commented 9 months ago

Hello everyone! I tried to implement the multiple trajectories, but it turns out that some theoretical work is still required, so it will be implemented in the future (perhaps during the next year or two).

So, we keep going with what we have right now! Thank you everyone for your work so far -- I am waiting for further comments and suggestions.

Best regards, Ioannis

strevezas commented 9 months ago

Review checklist for @strevezas

Conflict of interest

[x] 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

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/Mavrogiannis-Ioannis/dsmmR?
[x] License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@Mavrogiannis-Ioannis) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[x] 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

[x] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
[x] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
[x] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
[x] 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

[x] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[x] 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, who the target audience is, and its relation to other work?
[x] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[x] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[x] 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?

strevezas commented 9 months ago

Dear @Bisaloo ,

I have completed the revision of the paper. According to the package's github page and after running the relevant examples, almost everything seems to be in order. I have the following remarks:

Concerning the automated tests, already mentioned on the issue opened by you, should I check the relevant box? (https://github.com/Mavrogiannis-Ioannis/dsmmR/issues/2)
A code of conduct should be added, for example using the usethis::use_tidy_coc() function.

Best regards, Samis

Bisaloo commented 7 months ago

Hi @Mavrogiannis-Ioannis :wave:, this submission is almost through. Could you share a timeline for the last few outstanding points please:

for the tests, I went ahead and submitted https://github.com/Mavrogiannis-Ioannis/dsmmR/pull/4 to move this forwards. All you need to do now is:
- ~~fix the failing test~~
- [x] fix the example the failing test comes from
- ~~review and merge the PR~~
[x] for the code of conduct, as mentioned by @strevezas, a simple option is to use the Contributor Covenant Code of Conduct, which is added in a single line of code by running usethis::use_code_of_conduct().

Bisaloo commented 6 months ago

Hi @Mavrogiannis-Ioannis, could you please provide a timeline of when you'd be able to address the last minor issues mentioned in my previous comment?

In the absence of a reasonable timeline, our policy is to close the submission and ask to potentially resubmit later, but this means you'd have to restart the process.

Mavrogiannis-Ioannis commented 6 months ago

Dear @Bisaloo, Thank you for your continued support to push this submission forward. I will be able to finish the aforementioned tasks, with a limit at the end of June -- is it a reasonable time-frame or do we have to cut it shorter?

Best regards, Ioannis

Bisaloo commented 6 months ago

Yes, end of June is okay. Let's try and get this out of the door. Thanks!

Bisaloo commented 6 months ago

@editorialbot remind me in 5 weeks

editorialbot commented 6 months ago

Reminder set for @Bisaloo in 5 weeks

crvernon commented 6 months ago

@Bisaloo I am adding the paused tag to this so I don't keep checking in. Feel free to remove it when your reminder expires. Thanks!

Mavrogiannis-Ioannis commented 5 months ago

Good morning everyone,

Some progress has been made. Notably,

Code of Conduct now has been added according to your suggestions.
Tests have been implemented for the main functions of the package (I will merge the issues and pull requests in the next week @Bisaloo )
codecov github workflow has been established
Error fixed when printing a fit_dsmm() object, which specifically happened only in the parametric estimation case, while considering f not drifting (Model 2 case).

Therefore it seems to me that the main questions addressed above are dealt with. Do not hesitate to offer any other questions, ideas or suggestions.

Best regards, Ioannis

editorialbot commented 5 months ago

:wave: @Bisaloo, please take a look at the state of the submission (this is an automated reminder).

crvernon commented 4 months ago

:wave: @Bisaloo could you check back in on this one? Thanks.

Bisaloo commented 4 months ago

Hi :wave: @Mavrogiannis-Ioannis, thanks for the changes, and in particular the code of conduct and the tests. I also confirmed the print() issue I identified has been fixed.

@Athene-ai @RomainAzais @strevezas, could you please have a last look and confirm all seems good to go from your end? As a reminder, here are the links to your respective review comments, where the main issues seemed to be around the presence of tests:

Bisaloo commented 4 months ago

@editorialbot generate pdf

Bisaloo commented 4 months ago

@editorialbot check references

editorialbot commented 4 months ago

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

OK DOIs

- 10.2202/1544-6115.1326 is OK
- 10.1007/s11009-018-9682-8 is OK

MISSING DOIs

- No DOI given, and none found for title: Semi-Markov chains and hidden semi-Markov models t...
- No DOI given, and none found for title: smmR: Semi-Markov Models, Markov Models and Reliab...
- No DOI given, and none found for title: smmR: Simulation, Estimation and Reliability of Se...
- 10.32614/cran.package.drimmr may be a valid DOI for title: drimmR: Estimation, Simulation and Reliability of ...
- No DOI given, and none found for title: INVESTIGATING INDIVIDUAL PATHWAYS OF LEARNING: MOD...
- No DOI given, and none found for title: SemiMarkov: An R Package for Parametric Estimation...
- No DOI given, and none found for title: HMM: Hidden Markov Models
- No DOI given, and none found for title: HiddenMarkov: Hidden Markov Models
- No DOI given, and none found for title: Hidden Semi Markov Models for Multiple Observation...

INVALID DOIs

- None

editorialbot commented 4 months ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

Bisaloo commented 4 months ago

@Mavrogiannis-Ioannis, I have confirmed myself that the remaining uncheck boxes are now complete. Since they are very practical & objective points (presence of a code of conduct, presence of tests, etc.), I'm confident with moving forward with the acceptance of this submission.

I have proofread the article and submitted a pull request to fix minor formatting issues, and add the missing DOIs: https://github.com/Mavrogiannis-Ioannis/dsmmR/pull/5.

Once the PR is merged, could you please:

[x] Make a last proofreading pass from your end since the article will be published directly after acceptance by the editor in chief
[x] Make a tagged release of your software, and list the version tag of the archived version here.
[x] Archive the reviewed software in Zenodo or a similar service (e.g., figshare, an institutional repository)
[x] Check the archival deposit (e.g., in Zenodo) has the correct metadata. This includes the title (should match the paper title) and author list (make sure the list is correct and people who only made a small fix are not on it). You may also add the authors' ORCID.
[x] Please list the DOI of the archived version here.

I can then move forward with recommending acceptance of the submission.

Mavrogiannis-Ioannis commented 4 months ago

Dear @Bisaloo,

1) The proof-reading is done alongside with my co-authors, and small modifications were made for clarity. You can download it here: https://github.com/Mavrogiannis-Ioannis/dsmmR/actions/runs/10126436083. 2) The tagged release of the software is at: https://github.com/Mavrogiannis-Ioannis/dsmmR/releases/tag/v1.0.5 3) The reviewed software is in Zenodo, through the doi: https://zenodo.org/doi/10.5281/zenodo.13117522

Please, let me know for anything else necessary -- thank you so much for your work so far!

Respectfully, Ioannis

Bisaloo commented 4 months ago

@editorialbot generate pdf

Bisaloo commented 4 months ago

@editorialbot check references

editorialbot commented 4 months ago

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

OK DOIs

- 10.1007/978-0-387-73173-5 is OK
- 10.32614/CRAN.package.smmR is OK
- 10.2202/1544-6115.1326 is OK
- 10.1007/s11009-018-9682-8 is OK
- 10.32614/CRAN.package.drimmR is OK
- 10.18637/jss.v066.i06 is OK
- 10.32614/CRAN.package.HMM is OK
- 10.32614/CRAN.package.HiddenMarkov is OK
- 10.18637/jss.v039.i04 is OK

MISSING DOIs

- No DOI given, and none found for title: smmR: Semi-Markov Models, Markov Models and Reliab...
- No DOI given, and none found for title: INVESTIGATING INDIVIDUAL PATHWAYS OF LEARNING: MOD...

INVALID DOIs

- None

editorialbot commented 4 months ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

openjournals / joss-reviews