Comprehensive toolkit for addressing selection
bias in binary disease models across diverse non-probability samples, each
with unique selection mechanisms. It utilizes Inverse Probability Weighting
(IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce
selection bias effectively in multiple non-probability cohorts by integrating
data from either individual-level or summary-level external sources. The
package also provides a variety of variance estimation techniques. Please
refer to Kundu et al.