Package: optiscale 1.2.3

Dave Armstrong

optiscale: Optimal Scaling

Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. The targets are usually predicted values from a statistical model. If the data are nominal level, then the transformation must be identity-preserving. If the data are ordinal level, then the transformation must be monotonic. If the data are discrete, then tied data values must remain tied in the optimal transformation. If the data are continuous, then tied data values can be untied in the optimal transformation.

Authors:Dave Armstrong [aut, cre], William Jacoby [aut]

optiscale_1.2.3.tar.gz
optiscale_1.2.3.tar.gz(r-4.5-noble)optiscale_1.2.3.tar.gz(r-4.4-noble)
optiscale_1.2.3.tgz(r-4.4-emscripten)optiscale_1.2.3.tgz(r-4.3-emscripten)
optiscale.pdf |optiscale.html
optiscale/json (API)

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

Peer review:

Datasets:
  • elec92 - Public Opinion During the 1992 U.S. Presidential Election

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

1.18 score 1 stars 15 scripts 742 downloads 1 mentions 6 exports 1 dependencies

Last updated 6 months agofrom:8e1a9cd0d3. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-linuxOKNov 13 2024

Exports:calc.stressopscaleos.plotshep.plotshepardstress

Dependencies:lattice