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RegCalReliab: Regression Calibration Using Reliability Studies

Implements regression calibration methods for correcting measurement error in regression models using external or internal reliability studies. Methods are described in Carroll, Ruppert, Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear Models: A Modern Perspective" <doi:10.1201/9781420010138>.

Version: 0.2.0
Imports: stats, sandwich
Suggests: mgcv, knitr, rmarkdown
Published: 2025-10-06
DOI: 10.32614/CRAN.package.RegCalReliab
Author: Bowen Liu [aut, cre, cph], Yu Lu [aut], Molin Wang [aut]
Maintainer: Bowen Liu <bowenliu at hsph.harvard.edu>
BugReports: https://github.com/lbw080526/RegCalReliab/issues
License: MIT + file LICENSE
URL: https://lbw080526.github.io/RegCalReliab/, https://github.com/lbw080526/RegCalReliab
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: RegCalReliab results

Documentation:

Reference manual: RegCalReliab.html , RegCalReliab.pdf
Vignettes: regcal_example (source, R code)

Downloads:

Package source: RegCalReliab_0.2.0.tar.gz
Windows binaries: r-devel: RegCalReliab_0.2.0.zip, r-release: not available, r-oldrel: RegCalReliab_0.2.0.zip
macOS binaries: r-release (arm64): RegCalReliab_0.2.0.tgz, r-oldrel (arm64): RegCalReliab_0.2.0.tgz, r-release (x86_64): RegCalReliab_0.2.0.tgz, r-oldrel (x86_64): RegCalReliab_0.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=RegCalReliab to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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