Submitting Author Name: Philipp Wegmüller
Submitting Author Github Handle: @p-wegmueller
Other Package Authors Github handles: (comma separated, delete if none)
Repository: https://github.com/p-wegmueller/seasight
Submission type: Pre-submission
Language: en
- Paste the full DESCRIPTION file inside a code block below:
Package: seasight
Type: Package
Title: Tools for Seasonal Adjustment Diagnostics and Reporting
Version: 0.1.0
Depends:
R (>= 4.1.0)
Authors@R:
person("Philipp", "Wegmüller",
email = "philipp.wegmueller@seco.admin.ch",
role = c("aut", "cre"))
Description: Provides tools for automated diagnostics, model selection and
reporting for seasonal adjustment based on X-13ARIMA-SEATS. Wraps the
'seasonal' package to run and compare alternative specifications, extract
quality indicators and residual diagnostics, and generate reproducible
R Markdown reports for official statistics and other production workflows.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.3
Imports:
seasonal,
tsbox,
tibble,
dplyr,
purrr,
stringr,
htmltools,
glue
Suggests:
callr,
testthat (>= 3.0.0),
knitr,
rmarkdown,
units
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://github.com/p-wegmueller/seasight,
https://p-wegmueller.github.io/seasight/
BugReports: https://github.com/p-wegmueller/seasight/issues
Scope
seasight falls primarily under Time Series Analyses, because it supports diagnostic evaluation, model comparison, and reporting for seasonal-adjustment workflows based on X-13ARIMA-SEATS. It also falls partly under Exploratory Data Analysis and Summary Statistics, because it extracts, summarizes, ranks, and reports diagnostic indicators that help users compare alternative seasonal-adjustment specifications.
I am somewhat unsure whether the package should additionally be considered a workflow-support package or a scientific-software wrapper. It builds on the existing seasonal interface to X-13ARIMA-SEATS, but its main contribution is statistical decision support and reproducible diagnostic reporting rather than providing a new low-level interface to external software.
Yes. The package incorporates rOpenSci statistical standards documentation via the srr package. Locally, srr::srr_stats_pre_submit(path = ".", quiet = FALSE) returned:
All applicable standards [v0.2.0] have been documented in this package (153 complied with; 49 N/A standards).
- Who is the target audience and what are scientific applications of this package?
The target audience consists of applied time-series analysts, official statisticians, central-bank and government economists, and researchers who use seasonal adjustment in regular statistical production or empirical analysis. The package is especially relevant for users of X-13ARIMA-SEATS through R who need to compare alternative seasonal-adjustment specifications transparently and reproducibly.
Scientific applications include seasonal adjustment of economic and social time series; comparison of candidate X-13ARIMA-SEATS specifications; evaluation of trading-day, Easter, outlier, ARIMA, SEATS, and X-11 choices; assessment of residual seasonality and model stability; comparison with an incumbent production model; and generation of reproducible diagnostic reports. The package is intended to support transparent methodological decisions in statistical production and applied time-series research rather than to replace expert judgment.
There are several related packages in the seasonal-adjustment ecosystem, especially seasonal, [x13binary](https://cran.r-project.org/package=x13binary), and [RJDemetra](https://cran.r-project.org/package=RJDemetra), as well as JDemetra+. These packages provide access to seasonal-adjustment engines or interfaces to established seasonal-adjustment software.
seasight is complementary rather than directly duplicative. It does not introduce a new seasonal-adjustment engine and does not aim to replace seasonal, x13binary, RJDemetra, or JDemetra+. Instead, it adds a workflow layer for reproducible decision support: generating and running candidate seasonal-adjustment specifications, extracting diagnostics, ranking alternatives, comparing results against an incumbent model, and producing reproducible reports. The package therefore addresses the practical problem of documenting and reviewing seasonal-adjustment model choice in a systematic way.
Yes. The package operates on user-supplied time-series data and does not collect, transmit, or store personal data. It does not interact with external services, does not require user credentials, and does not include telemetry. The package is methodologically oriented toward seasonal adjustment and time-series diagnostics. Any ethical or privacy considerations related to the input data remain the responsibility of the user and their institution; the package itself does not include human-subject data or personally identifiable information.
- Any other questions or issues we should be aware of?:
The main question for this pre-submission inquiry is whether seasight is considered in scope for rOpenSci Statistical Software Peer Review. I believe it fits primarily under Time Series Analyses, with a secondary connection to Exploratory Data Analysis and Summary Statistics.
The package is already fairly mature for scope assessment. It includes documented exported functions, examples, tests, vignettes/reports, CI workflows, code coverage, and srr standards documentation. Locally, autotest::autotest_package(".", test = TRUE) reported no failing autotests; it returned warnings/diagnostics only, with all reported rows having test = TRUE. Local pkgcheck() and R CMD check were also run during pre-submission preparation. One local note related to the missing tidy executable for HTML manual validation appears to be machine-specific. A license-related note connected to x13binary is inherited through the package’s dependence on the X-13ARIMA-SEATS ecosystem.
Use of Generative AI
If so, please describe usage, and include links to any relevant aspects of your repository. See [our blog post](https://ropensci.org/blog/2026/02/26/ropensci-ai-policy/) for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)
Generative AI tools were used as development assistants during the final pre-submission phase. In particular, ChatGPT and Codex were used to help inspect package readiness, identify possible documentation and packaging issues, draft parts of the submission text, and suggest small repository-quality improvements. All changes were reviewed by the submitting author and validated through the package’s tests and R CMD check workflow. Local Codex/agent configuration files were removed from Git tracking and are not part of the submitted repository.
Submitting Author Name: Philipp Wegmüller
Submitting Author Github Handle: @p-wegmueller
Other Package Authors Github handles: (comma separated, delete if none)
Repository: https://github.com/p-wegmueller/seasight
Submission type: Pre-submission
Language: en
Scope
Please indicate which category or categories from our [package fit policies](https://ropensci.github.io/dev_guide/policies.html#package-categories) or [statistical package categories](https://stats-devguide.ropensci.org/overview.html#overview-categories) this package falls under. (Please check one or more appropriate boxes below):
Data Lifecycle Packages
Statistical Packages
Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:
seasightfalls primarily under Time Series Analyses, because it supports diagnostic evaluation, model comparison, and reporting for seasonal-adjustment workflows based on X-13ARIMA-SEATS. It also falls partly under Exploratory Data Analysis and Summary Statistics, because it extracts, summarizes, ranks, and reports diagnostic indicators that help users compare alternative seasonal-adjustment specifications.I am somewhat unsure whether the package should additionally be considered a workflow-support package or a scientific-software wrapper. It builds on the existing
seasonalinterface to X-13ARIMA-SEATS, but its main contribution is statistical decision support and reproducible diagnostic reporting rather than providing a new low-level interface to external software.Yes. The package incorporates rOpenSci statistical standards documentation via the
srrpackage. Locally,srr::srr_stats_pre_submit(path = ".", quiet = FALSE)returned:The target audience consists of applied time-series analysts, official statisticians, central-bank and government economists, and researchers who use seasonal adjustment in regular statistical production or empirical analysis. The package is especially relevant for users of X-13ARIMA-SEATS through R who need to compare alternative seasonal-adjustment specifications transparently and reproducibly.
Scientific applications include seasonal adjustment of economic and social time series; comparison of candidate X-13ARIMA-SEATS specifications; evaluation of trading-day, Easter, outlier, ARIMA, SEATS, and X-11 choices; assessment of residual seasonality and model stability; comparison with an incumbent production model; and generation of reproducible diagnostic reports. The package is intended to support transparent methodological decisions in statistical production and applied time-series research rather than to replace expert judgment.
There are several related packages in the seasonal-adjustment ecosystem, especially
seasonal,[x13binary](https://cran.r-project.org/package=x13binary), and[RJDemetra](https://cran.r-project.org/package=RJDemetra), as well as JDemetra+. These packages provide access to seasonal-adjustment engines or interfaces to established seasonal-adjustment software.seasightis complementary rather than directly duplicative. It does not introduce a new seasonal-adjustment engine and does not aim to replaceseasonal,x13binary,RJDemetra, or JDemetra+. Instead, it adds a workflow layer for reproducible decision support: generating and running candidate seasonal-adjustment specifications, extracting diagnostics, ranking alternatives, comparing results against an incumbent model, and producing reproducible reports. The package therefore addresses the practical problem of documenting and reviewing seasonal-adjustment model choice in a systematic way.Yes. The package operates on user-supplied time-series data and does not collect, transmit, or store personal data. It does not interact with external services, does not require user credentials, and does not include telemetry. The package is methodologically oriented toward seasonal adjustment and time-series diagnostics. Any ethical or privacy considerations related to the input data remain the responsibility of the user and their institution; the package itself does not include human-subject data or personally identifiable information.
The main question for this pre-submission inquiry is whether
seasightis considered in scope for rOpenSci Statistical Software Peer Review. I believe it fits primarily under Time Series Analyses, with a secondary connection to Exploratory Data Analysis and Summary Statistics.The package is already fairly mature for scope assessment. It includes documented exported functions, examples, tests, vignettes/reports, CI workflows, code coverage, and
srrstandards documentation. Locally,autotest::autotest_package(".", test = TRUE)reported no failing autotests; it returned warnings/diagnostics only, with all reported rows havingtest = TRUE. Localpkgcheck()and R CMD check were also run during pre-submission preparation. One local note related to the missingtidyexecutable for HTML manual validation appears to be machine-specific. A license-related note connected tox13binaryis inherited through the package’s dependence on the X-13ARIMA-SEATS ecosystem.Use of Generative AI
If so, please describe usage, and include links to any relevant aspects of your repository. See [our blog post](https://ropensci.org/blog/2026/02/26/ropensci-ai-policy/) for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)
Generative AI tools were used as development assistants during the final pre-submission phase. In particular, ChatGPT and Codex were used to help inspect package readiness, identify possible documentation and packaging issues, draft parts of the submission text, and suggest small repository-quality improvements. All changes were reviewed by the submitting author and validated through the package’s tests and R CMD check workflow. Local Codex/agent configuration files were removed from Git tracking and are not part of the submitted repository.