Presubmission inquiry: dynamite - Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

[santikka]

Submitting Author Name: Santtu Tikka
Submitting Author Github Handle: @santikka
Other Package Authors Github handles: @helske
Repository: https://github.com/santikka/dynamite
Submission type: Pre-submission
Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Package: dynamite
Title: Bayesian Modeling and Causal Inference for Multivariate
    Longitudinal Data
Version: 0.0.1
Authors@R: c(
    person("Santtu", "Tikka", , "santtuth@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-4039-4342")),
    person("Jouni", "Helske", , "jouni.helske@iki.fi", role = "aut",
           comment = c(ORCID = "0000-0001-7130-793X"))
  )
Description: Easy-to-use and efficient interface for 
  Bayesian inference of complex panel (time series) data. The package supports 
  joint modeling of multiple measurements per individual, time-varying and
  time-invariant effects, and a wide range of discrete and 
  continuous distributions. Estimation of the models is carried out via 'Stan'.
License: GPL (>= 3)
URL: https://github.com/santikka/dynamite
BugReports: https://github.com/santikka/dynamite/issues
Depends: 
    R (>= 4.1.0)
Imports: 
    bayesplot,
    checkmate,
    cli,
    data.table (>= 1.14.3),
    dplyr,
    glue,
    ggplot2,
    MASS,
    posterior,
    rlang,
    rstan (>= 2.26.11),
    stats,
    tidyr,
    utils
Suggests: 
    covr,
    knitr,
    plm,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: 
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd",
    "srr::srr_stats_roclet"))
RoxygenNote: 7.2.1
LazyData: true
LazyDataCompression: xz

Scope

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):

  Data Lifecycle Packages

  • data retrieval
  • data extraction
  • data munging
  • data deposition
  • data validation and testing
  • workflow automation
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

The package performs multivariate bayesian regression modeling for panel data (multiple individuals measured over time). Model estimation is carried out via Stan.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

Yes.

• Who is the target audience and what are scientific applications of this package?

The package is mainly intended for applied researchers working with complex panel data. Panel data is common in many scientific fields, especially in sociology and econometrics. For example, analysing individual-level life-course data is valuable for assessing the effects of policy reforms and other interventions.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

Several approaches and R packages exist for panel data analysis, that share some common features with dynamite, which we have listed in the package README along with the full feature suite of the package. However, we found that other packages often fall short in crucial aspects, such as only supporting gaussian responses, univariate data or effects constant over time and are thus applicable only in specific constrained scenarios. To the best of our knowledge, there are no other R packages (or software in general) that support all features of dynamite simultaneously.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

Yes.

• Any other questions or issues we should be aware of?:

Regarding the standards, G1.0 states that:

"G1.0 Statistical Software should list at least one primary reference from published academic literature."

While we certainly have several references in the package vignette, our plan is to also write two scientific papers ourselves: a methodological paper that would serve as the primary reference, and a software paper focusing on the dynamite package (possibly in the Journal of Statistical Software). Would it be an issue that this "primary reference" does not yet exist at the review stage?

[adamhsparks]

Hi @santikka,
In reference to your query about the publication requirement. If I understand your inquiry correctly, you have several references for the methods that you are using in the package but this package is the first to combine them, is this correct?

[santikka]

Hi @adamhsparks,
Yes, that is correct.

[adamhsparks]

That’s fine. If you have those references for the methods then we can accept your submission.

[santikka]

Hi @adamhsparks,
Please let me know if there is anything I can do to move this inquiry forward. Should I provide the references mentioned here?

[adamhsparks]

Hi @santikka, I've updated the label on this just for clarity. I had missed that we actually have a "0/presubmission" label.

To move forward, please open a new issue with your submission.

I am closing this now and look forward to your submission.

[santikka]

Thanks @adamhsparks! I will be submitting the package very soon.