Selection and Misclassification Bias Adjustment for Logistic Regression Models R package SAMBA version 1.0.0 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. Software Imports: stats, optimx, survey Suggests: knitr, rmarkdown, ggplot2, scales, MASS Lauren Beesley <lbeesley@umich.edu> Comprehensive R Archive Network (CRAN) Lauren Beesley [cre], Alexander Rix [aut] GPL-3 2026-06-03 application/tgz https://CRAN.R-project.org/package=SAMBA doi:10.32614/CRAN.package.SAMBA