Package: SAMBA
Title: Selection and Misclassification Bias Adjustment for Logistic
        Regression Models
Version: 1.0.0
Authors@R: c(
    person("Lauren", "Beesley", email = "lbeesley@umich.edu", role = "cre"),
    person("Alexander", "Rix", email = "alexrix@umich.edu", role = "aut")
    )
Description: 
    Health research using data from electronic health records (EHR) has gained
    popularity, but misclassification of EHR-derived disease status and lack of
    representativeness of the study sample can result in substantial bias in
    effect estimates and can impact power and type I error for association
    tests. Here, the assumed target of inference is the relationship between
    binary disease status and predictors modeled using a logistic regression
    model. 'SAMBA' implements several methods for obtaining bias-corrected
    point estimates along with valid standard errors as proposed in Beesley and
    Mukherjee (2020) <doi:10.1111/biom.13400>, Biometrics. 
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Imports: stats, optimx, survey
Suggests: knitr, rmarkdown, ggplot2, scales, MASS
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-02 21:36:26 UTC; lbeesley
Author: Lauren Beesley [cre],
  Alexander Rix [aut]
Maintainer: Lauren Beesley <lbeesley@umich.edu>
Repository: CRAN
Date/Publication: 2026-06-03 08:50:20 UTC
Built: R 4.6.0; ; 2026-06-03 12:56:14 UTC; unix
