Package: seasight 0.1.0

Philipp Wegmüller

seasight: Tools for Seasonal Adjustment Diagnostics and Reporting

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.

Authors:Philipp Wegmüller [aut, cre]

seasight_0.1.0.tar.gz
seasight_0.1.0.zip(r-4.7-any)seasight_0.1.0.zip(r-4.6-any)seasight_0.1.0.zip(r-4.5-any)
seasight_0.1.0.tgz(r-4.6-any)seasight_0.1.0.tgz(r-4.5-any)
seasight_0.1.0.tar.gz(r-4.7-any)seasight_0.1.0.tar.gz(r-4.6-any)
seasight_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
seasight/json (API)

# Install 'seasight' in R:
install.packages('seasight', repos = c('https://ropensci-staging.r-universe.dev', 'https://cloud.r-project.org'))

Reviews:rOpenSci Software Review #()

Bug tracker:https://github.com/p-wegmueller/seasight/issues

Pkgdown/docs site:https://p-wegmueller.github.io

On CRAN:

Conda:

5.10 score 5 stars 8 scripts 16 exports 29 dependencies

Last updated from:a89947bd5f. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK161
pkgdown docsOK300
source / vignettesOK210
linux-releaseOK165
macos-releaseOK118
macos-oldrelOK135
windows-develOK149
windows-releaseOK155
windows-oldrelOK150
wasm-releaseOK110

Exports:auto_seasonal_analysisbuild_user_xregextract_outliersprobe_casesa_align_regressorsa_batch_runsa_comparesa_copyable_callsa_engine_choice_cardsa_existence_callsa_existence_cardsa_is_do_not_adjustsa_report_htmlsa_should_switchsa_tests_modelsa_top_candidates_table

Dependencies:anytimebase64encBHclidata.tabledigestdplyrfastmapgenericsgluehtmltoolslifecyclemagrittrpillarpkgconfigpurrrR6Rcpprlangseasonalstringistringrtibbletidyselecttsboxutf8vctrswithrx13binary

Advanced usage: baselines, TD candidates & decision rules
1. Baselines: comparing against your current production model | 1.1 Visual comparison | 1.2 Numeric comparison: sa_top_candidates_table() and sa_compare() | 2. Providing calendar regressors (trading days, moving holidays) | 2.1 Precomputed regressors (example: length-of-month) | 2.2 Moving-holiday pulses via build_user_xreg() (Genhol-style) | 2.3 SEATS and “future” xreg values (why padding exists) | 3. Decision rules: existence of seasonality & switching models | 3.1 “DO_NOT_ADJUST” vs “adjust” | 3.2 “Keep current model” vs “switch to new model” | 3.3 Putting it together: a minimal decision wrapper | 4. Further reading
Rendered fromseasight-advanced.Rmdusingknitr::rmarkdown

Last update: 2026-09-14
Started: 2025-11-28

Calendar and holiday regressors
Built-in Easter handling | Precomputed trading-day regressors | Moving holidays with build_user_xreg() | Inspecting the final decision
Rendered fromseasight-calendar-holidays.Rmdusingknitr::rmarkdown

Last update: 2026-09-14
Started: 2026-04-21

Getting started with seasight
How this package differs from alternatives | Setup | A first seasonal adjustment with seasonal | Low-level diagnostics: sa_tests_model() | Automatic model search: auto_seasonal_analysis() | Calendar regressors (trading days & moving holidays) | Inspecting the winning model | Visual “cards”: sa_existence_card() and sa_engine_choice_card() | From analysis to decision: sa_should_switch() | A full HTML report in one call: sa_report_html() | Where to go next
Rendered fromseasight-getting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-14
Started: 2025-11-28

Seasonal adjustment in practice: implementing common recommendations with seasight
2. Part I – Internationally common recommendations | 2.1 When is seasonal adjustment appropriate? | 2.2 Building models that are parsimonious and stable | 2.3 Calendar effects: trading days and moving holidays | 2.4 Stability, revisions and model review | 2.5 Documentation, transparency and reproducibility | 3. Part II – Implementing the recommendations with seasight | 3.1 Deciding whether to seasonally adjust | 3.2 Parsimonious and stable models | 3.3 Calendar effects: trading days, Easter, and user xregs | 3.3.1 Supplying calendar regressors via td_candidates | 3.3.2 A practical note on SEATS and future xreg values | 3.3.3 Inspecting what won (and why) | 3.4 Stability, revisions and model review | 3.5 Documentation, transparency and reproducibility
Rendered fromseasight-sa-practice.Rmdusingknitr::rmarkdown

Last update: 2026-09-14
Started: 2025-12-02

Interpreting an absent SEATS seasonal component
What "absent" means | How seasight uses the signal | Minimal inspection pattern
Rendered fromseasight-seats-absent.Rmdusingknitr::rmarkdown

Last update: 2026-04-20
Started: 2026-04-20