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.

Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The 'surveydata' package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.

Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. 
    Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and 
    to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data.

A tool for computing network representations of attitudes,
  extracted from tabular data such as sociological surveys. By treating
  a survey as a bipartite network, we measure the similarity between
  respondents and survey items to produce network edges. We do this in both a
  respondent network, as well as a survey item network. Used in combination
  with graph visualisation libraries, this technique helps practitioners in the
  social sciences identify network structure that may be present within a
  survey.

Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.