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forecasting vs rempsyc

A side-by-side editorial comparison of forecasting and rempsyc — release velocity, themes, recent moves, and the top alternatives to consider.

forecasting vs rempsyc: at a glance

Featureforecastingrempsyc
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesforecasting, epidemiology, reproducibility, vignettesapa-formatting, psychology-research, statistical-tables, ggplot2
Last editorial update7h ago1h ago
WebsiteVisit →Visit →

What is forecasting?

HIDDA.forecasting is a book chapter's reproducibility artifact, not a package under development.

HIDDA.forecasting accompanies a book chapter on forecasting infectious disease counts; its vignettes reproduce the results presented there using arima, prophet, glarma, hhh4contacts and scoringRules. The 1.0.0 release states this outright — it is the version used for the chapter, pinned to CRAN package versions as of July 2018. Every release since has been a vignette rebuild against newer R and dependency versions.

Read the full forecasting trajectory →

What is rempsyc?

Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.

rempsyc produces APA-formatted tables and figures for psychology research — nice_table() for results tables, plus plotting helpers for scatter plots, violin plots, densities and simple slopes. Its releases are CRAN submissions that bundle a long run of development versions, so each entry reads as a digest rather than a single change. The most recent, 0.2.0, added point labelling and per-group correlation statistics to nice_scatter and fixed nice_lm() failing on factor covariates with more than two levels.

Read the full rempsyc trajectory →

forecasting vs rempsyc: editorial side-by-side

F
forecasting
ANALYTICS
0.0

HIDDA.forecasting is a book chapter's reproducibility artifact, not a package under development.

◆ Current state

HIDDA.forecasting accompanies a book chapter on forecasting infectious disease counts; its vignettes reproduce the results presented there using arima, prophet, glarma, hhh4contacts and scoringRules. The 1.0.0 release states this outright — it is the version used for the chapter, pinned to CRAN package versions as of July 2018. Every release since has been a vignette rebuild against newer R and dependency versions.

◆ Where it's heading

The release pattern is maintenance on an eight-year cadence dictated entirely by the surrounding ecosystem: 1.1.1 rebuilt under R 4.0.4, 1.1.2 under R 4.3.2, 1.1.3 under R 4.6.1, each reporting whether the numbers moved. They mostly have not — the recurring note is minor numerical differences confined to the prophet forecasts in vignette('CHILI_prophet'). The only substantive change in the visible history is 1.1.0's methodological tidy-up of the scoring comparisons.

◆ Prediction

Nothing in these entries points to new functionality; the next release is most likely another vignette rebuild whenever a dependency change or a CRAN check failure forces one.

R
rempsyc
ANALYTICS
0.0

Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.

◆ Current state

rempsyc produces APA-formatted tables and figures for psychology research — nice_table() for results tables, plus plotting helpers for scatter plots, violin plots, densities and simple slopes. Its releases are CRAN submissions that bundle a long run of development versions, so each entry reads as a digest rather than a single change. The most recent, 0.2.0, added point labelling and per-group correlation statistics to nice_scatter and fixed nice_lm() failing on factor covariates with more than two levels.

◆ Where it's heading

Two forces drive this package and neither is its own roadmap. The first is APA style: when the 7th edition advised against beta for standardized coefficients, the package switched its output to italic b with an asterisk. The second is the surrounding ecosystem — formatting is aligned to what lavaanExtra and afex produce, contrast handling was delegated to easystats' modelbased, and Excel correlation matrix export was handed entirely to the correlation package to cut maintenance.

◆ Prediction

The pattern of delegating functionality to specialist packages while keeping the formatting layer is well established and likely continues. Because releases bundle many small dev versions, the next one will probably again mix plotting refinements with fixes surfaced by upstream changes.

Alternatives to forecasting and rempsyc

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either forecasting or rempsyc.

See all forecasting alternatives → · See all rempsyc alternatives →

Recent activity from forecasting and rempsyc

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agoforecastingVignettes rebuilt under R 4.6.1
  2. 11mo agorempsycPoint labels and per-group correlations added to nice_scatter
  3. 1y agorempsycExcel correlation export delegated to the correlation package
  4. 2y agorempsycTable spacing control and a fix for name collision with afex
  5. 2y agoforecastingVignettes rebuilt under R 4.3.2
  6. 2y agorempsycStandardized coefficients switch to APA 7th edition b* notation
  7. 2y agorempsycLegend and standardization-check fixes
  8. 2y agorempsycnice_table starts coercing model objects automatically
  9. 5y agoforecastingVignettes rebuilt under R 4.0.4
  10. 7y agoforecastingStandard PIT and discretized log-normal scoring
  11. 7y agoforecastingThe version used for the book chapter, with pinned dependencies

Frequently asked questions

What is the difference between forecasting and rempsyc?

They serve adjacent needs but don't currently overlap on shipped themes. forecasting and rempsyc are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is forecasting better than rempsyc?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. forecasting and rempsyc are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to forecasting?

Top forecasting alternatives in Analytics are ranked by recent ship velocity. Browse the "forecasting alternatives" section above for the current picks, or visit /alternatives/forecasting for the full list with editorial commentary on each.

What are the best alternatives to rempsyc?

Top rempsyc alternatives in Analytics are ranked by recent ship velocity. Browse the "rempsyc alternatives" section above for the current picks, or visit /alternatives/rempsyc for the full list with editorial commentary on each.