mvPred - Methods for Handling Missing Values in Linear Modeling

Provides user-friendly methods for handling missing data in regression modeling, including available-case linear regression, multiple imputation, random-forest imputation, and the Tower method. Implemented approaches include chained-equation imputation described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>, multiple imputation described by Honaker, King and Blackwell (2011) <doi:10.18637/jss.v045.i07>, random-forest imputation described by Stekhoven and Buehlmann (2012) <doi:10.1093/bioinformatics/btr597>, and the Tower method described by Matloff and Mohanty (2023) <https://CRAN.R-project.org/package=toweranNA>.