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: 2026-09-23 07:09:22 UTC; root
Author: Lauren Beesley [cre], Alexander Rix [aut]
Maintainer: Lauren Beesley <lbeesley@umich.edu>
Config/pak/sysreqs: cmake make
Repository: https://lbeesleybiostat.r-universe.dev
Date/Publication: 2026-06-03 12:15:30 UTC
RemoteUrl: https://github.com/cran/SAMBA
RemoteRef: HEAD
RemoteSha: d057ac0d7b8355c0f61a87564ba82617c5d38aa4