Added helper function

`as_data_frame_with_weights()`

to convert a survey design object into a data frame with columns of weights (full-sample weights and, if applicable, replicate weights). This is useful for saving data and weights to a data file.Added

`by`

argument to`summarize_rep_weights()`

which allows the specification of one or more grouping variables to use for summaries (e.g.`by = c('stratum', 'response_status')`

can be used to summarize by response status within each stratum).Added a small vignette “Nonresponse Adjustments” to illustrate how to conduct nonresponse adjustments using

`redistribute_weights()`

.Minor Updates and Bug Fixes:

- Internal code update to avoid annoying but harmless warning message
about
`rho`

in`calibrate_to_estimate()`

. - Bug fix for
`stack_replicate_designs()`

where designs created with`as.svrepdesign(..., type = 'mrbbootstrap')`

or`as.svrepdesign(..., type = 'subbootstrap')`

threw an error.

- Internal code update to avoid annoying but harmless warning message
about

Added functions

`calibrate_to_estimate()`

and`calibrate_to_sample()`

for calibrating to estimated control totals with methods that account for the sampling variance of the control totals. For an overview of these functions, please see the new vignette “Calibrating to Estimated Control Totals”.The function

`calibrate_to_estimate()`

requires the user to supply a vector of control totals and its variance-covariance matrix. The function applies Fuller’s proposed adjustments to the replicate weights, in which control totals are varied across replicates by perturbing the control totals using a spectral decomposition of the control totals’ variance-covariance matrix.The function

`calibrate_to_sample()`

requires the user to supply a replicate design for the primary survey of interest as well as a replicate design for the control survey used to estimate control totals for calibration. The function applies Opsomer & Erciulescu’s method of varying the control totals across replicates of the primary survey by matching each primary survey replicate to a replicate from the control survey.

Added an example dataset,

`lou_vax_survey`

, which is a simulated survey measuring Covid-19 vaccination status and a handful of demographic variables, based on a simple random sample of 1,000 residents of Louisville, Kentucky with an approximately 50% response rate.- An accompanying dataset
`lou_pums_microdata`

provides person-level microdata from the American Community Survey (ACS) 2015-2019 public-use microdata sample (PUMS) data for Louisville, KY. The dataset`lou_pums_microdata`

includes replicate weights to use for variance estimation and can be used to generate control totals for`lou_vax_survey`

.

- An accompanying dataset

- Initial release of the package.