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rempsyc vs vim

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

Shared themes:r-package

rempsyc vs vim: at a glance

Featurerempsycvim
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesapa-formatting, psychology-research, statistical-tables, ggplot2r-package, missing-data, imputation, correctness-audit
Last editorial update52m ago4h ago
WebsiteVisit →Visit →

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 →

What is vim?

Six dormant years end with a correctness audit across VIM's entire imputation surface

VIM handles visualization and imputation of missing values in R, with kNN, hot-deck, iterative robust model-based imputation and matching-based methods. Development effectively stopped after 6.0.0 in 2020. Version 7.2.0 arrives in July 2026 as an explicitly framed correctness milestone: MI-properness warnings, ordered-factor preservation, a keep_all_columns option, list returns from irmi(mi>1), repairs to imputeRobust and imputeRobustChain, cellwise IRWLS and initial-weight fixes, and kNN and gowerD mixed-scaling corrections with a weightDist guard.

Read the full vim trajectory →

rempsyc vs vim: editorial side-by-side

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.

V
vim
ANALYTICS
0.0

Six dormant years end with a correctness audit across VIM's entire imputation surface

◆ Current state

VIM handles visualization and imputation of missing values in R, with kNN, hot-deck, iterative robust model-based imputation and matching-based methods. Development effectively stopped after 6.0.0 in 2020. Version 7.2.0 arrives in July 2026 as an explicitly framed correctness milestone: MI-properness warnings, ordered-factor preservation, a keep_all_columns option, list returns from irmi(mi>1), repairs to imputeRobust and imputeRobustChain, cellwise IRWLS and initial-weight fixes, and kNN and gowerD mixed-scaling corrections with a weightDist guard.

◆ Where it's heading

The release notes describe an audit — Wave 1 plus tail — rather than a feature cycle, and the fixes cluster around statistical validity: whether multiple imputation is proper, whether factor ordering survives, whether distance scaling across mixed variable types is right. Those are the properties users cannot easily verify themselves, so a package correcting them after six years is implicitly restating what its earlier output was worth. The notes also name a forthcoming R Journal paper under the name vimpute, which points at a successor or companion identity.

◆ Prediction

The entries call this a stable reference point for a paper and refer to Wave 1, so a further audit wave is the most likely next release; the vimpute naming is worth watching but the entries do not say what it is.

Alternatives to rempsyc and vim

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 rempsyc or vim.

See all rempsyc alternatives → · See all vim alternatives →

Recent activity from rempsyc and vim

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

  1. 1mo agovimCorrectness audit fixes MI-properness, factor order and distance scaling
  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 agorempsycStandardized coefficients switch to APA 7th edition b* notation
  6. 2y agorempsycLegend and standardization-check fixes
  7. 2y agorempsycnice_table starts coercing model objects automatically
  8. 6y agovimAdds ranger-based imputation, drops survey and GUI support
  9. 6y agovimAdds nine example datasets and splits help pages
  10. 6y agovimAdds matchImpute() and random-forest augmented kNN
  11. 6y agovimOrdered factor support and ordinal regression in irmi()
  12. 6y agovimBug fixes for kNN, hotdeck and irmi input handling

Frequently asked questions

What is the difference between rempsyc and vim?

Both compete on the same themes — r-package — within Analytics. rempsyc and vim 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 rempsyc better than vim?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rempsyc and vim 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 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.

What are the best alternatives to vim?

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