Cross Validation Based on Survey Design R package surveyCV version 0.2.0 Functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) <doi:10.1002/sta4.454> explains why differing how we take folds based on survey design is useful. Software Depends: R (>= 4.0) Imports: survey (>= 4.1), magrittr (>= 2.0) Suggests: dplyr (>= 1.0), ggplot2 (>= 3.3), grid (>= 4.0), gridExtra (>= 2.3), ISLR (>= 1.2), knitr (>= 1.29), rmarkdown (>= 2.2), rpms (>= 0.5), splines (>= 4.0), testthat (>= 3.1) Jerzy Wieczorek <jawieczo@colby.edu> Comprehensive R Archive Network (CRAN) Cole Guerin [aut], Thomas McMahon [aut], Jerzy Wieczorek [cre, aut] (ORCID: <https://orcid.org/0000-0002-2859-6534>), Hunter Ratliff [ctb] GPL-2 GPL-3 2022-03-15 application/tgz https://CRAN.R-project.org/package=surveyCV doi:10.32614/CRAN.package.surveyCV