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surveyCV

Cross Validation Based on Survey Design

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.

Versions across snapshots

VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 surveyCV_0.2.0.tar.gz 746.1 KiB
0.2.0 rolling linux/noble R-4.5 surveyCV_0.2.0.tar.gz 746.0 KiB
0.2.0 rolling source/ R- surveyCV_0.2.0.tar.gz 742.5 KiB
0.2.0 latest linux/jammy R-4.5 surveyCV_0.2.0.tar.gz 746.1 KiB
0.2.0 latest linux/noble R-4.5 surveyCV_0.2.0.tar.gz 746.0 KiB
0.2.0 latest source/ R- surveyCV_0.2.0.tar.gz 742.5 KiB
0.2.0 2026-04-26 source/ R- surveyCV_0.2.0.tar.gz 742.5 KiB
0.2.0 2026-04-23 source/ R- surveyCV_0.2.0.tar.gz 742.5 KiB
0.2.0 2026-04-09 windows/windows R-4.5 surveyCV_0.2.0.zip 751.6 KiB
0.2.0 2025-04-20 source/ R- surveyCV_0.2.0.tar.gz 742.5 KiB

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