Package: Keng 2026.3.19.9000

Keng: Knock Errors Off Nice Guesses

Miscellaneous functions and data used in psychological research and teaching. Keng currently has four built-in datasets, and could (1) scale a vector; (2) divide a vector into three groups, (3) compute the cut-off values of Pearson's r with known sample size; (4) test the significance and compute the post-hoc power for Pearson's r with known sample size; (5) conduct a priori power analysis and plan the sample size for Pearson's r; (6) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or partial Eta-squared); (7) calculate PRE from partial correlation, Cohen's f, or f_squared; (8) conduct a priori power analysis and plan the sample size for one or a set of predictors in regression analysis; (9) conduct post-hoc power analysis for one or a set of predictors in regression analysis with known sample size; (10) randomly pick numbers for Chinese Super Lotto and Double Color Balls; (11) assess course objective achievement in Outcome-Based Education.

Authors:Qingyao Zhang [aut, cre]

Keng_2026.3.19.9000.tar.gz
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Keng_2026.3.19.9000.tgz(r-4.6-any)Keng_2026.3.19.9000.tgz(r-4.5-any)
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Keng_2026.3.19.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
Keng/json (API)

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

Bug tracker:https://github.com/qyaozh/keng/issues

Datasets:
  • depress - Three-wave data from the D research
  • depress1 - The 1st wave data from the D research
  • depress2 - The 2nd wave data from the D research
  • depress3 - The 3rd wave data from the D research
  • well - Three-wave data from the W research
  • well1 - The 1st wave data from the W research
  • well2 - The 2nd wave data from the W research
  • well3 - The 3rd wave data from the W research

On CRAN:

Conda:

5.97 score 22 scripts 607 downloads 13 exports 0 dependencies

Last updated from:be5e392355. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING147
source / vignettesOK201
linux-release-x86_64WARNING133
macos-release-arm64WARNING85
macos-oldrel-arm64WARNING96
windows-develWARNING91
windows-releaseWARNING77
windows-oldrelWARNING90
wasm-releaseOK148

Exports:assess_coacalc_PREcompare_lmcut_rdividepick_dcbpick_slpower_lmpower_rpowered_lmpowered_rScaletest_r

Dependencies:

PRE

Last update: 2026-04-13
Started: 2024-11-06

Partial Regression
Multiple regression with t-test | Hierarchical regression with F-test | The PRE of the single parameter | One-predictor regression using the residuals

Last update: 2026-04-12
Started: 2024-11-05

Plan Sample Size
Understand the arguments of power_lm() | PRE | PA and PC | Application | A set of predictors | A continuous predictor | Moderation model | One-sample t -test or an intercept-only regression | Two-sample t -test or a binary predictor | ANOVA or a multicategorical predictor | ANOVA concerning repeated measures | Reference

Last update: 2026-04-12
Started: 2024-11-24

assessCOA

Last update: 2025-10-08
Started: 2025-08-31

Common Sample Sizes
Use Pearson's r | Mediation | Moderation | ANOVA | Control for other covariates | Conclusion | Reference

Last update: 2025-08-31
Started: 2024-12-24