Package: wec 0.4-1

wec: Weighted Effect Coding

Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.

Authors:Rense Nieuwenhuis, Manfred te Grotenhuis, Ben Pelzer, Alexander Schmidt, Ruben Konig, Rob Eisinga

wec_0.4-1.tar.gz
wec_0.4-1.zip(r-4.5)wec_0.4-1.zip(r-4.4)wec_0.4-1.zip(r-4.3)
wec_0.4-1.tgz(r-4.5-any)wec_0.4-1.tgz(r-4.4-any)wec_0.4-1.tgz(r-4.3-any)
wec_0.4-1.tar.gz(r-4.5-noble)wec_0.4-1.tar.gz(r-4.4-noble)
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wec.pdf |wec.html
wec/json (API)

# Install 'wec' in R:
install.packages('wec', repos = c('https://rensenieuwenhuis.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • BMI - Data on BMI of Dutch citizens
  • PUMS - Public Use Microdata Sample files (PUMS) 2013

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.53 score 2 stars 17 scripts 596 downloads 2 exports 16 dependencies

Last updated 7 years agofrom:64f2cead36. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 13 2025
R-4.5-winOKMar 13 2025
R-4.5-macOKMar 13 2025
R-4.5-linuxOKMar 13 2025
R-4.4-winOKMar 13 2025
R-4.4-macOKMar 13 2025
R-4.4-linuxOKMar 13 2025
R-4.3-winOKMar 13 2025
R-4.3-macOKMar 13 2025

Exports:contr.wecwec.interact

Dependencies:clidplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigR6rlangtibbletidyselectutf8vctrswithr