Changelog

Source: NEWS.md

surveycore 0.6.2

Bug fixes

• Moved dplyr from Suggests to Imports (used unguarded in metadata functions).
• Fixed broken vignette("estimation") cross-reference in creating-survey-objects vignette.
• Fixed non-canonical CRAN URLs in surveycore-vs-survey vignette.

Documentation

• Updated README to reflect current API: as_survey_replicate() (not as_survey_rep()), added get_diffs(), survey_glm(), and survey_nonprob.
• Added @examples to 12 exported functions and @return to survey_base for CRAN compliance.

surveycore 0.6.1

Bug fixes

• survey_nonprob validator now accepts zero weights when at least one positive weight exists, unblocking the surveywts adjust_nonresponse() workflow. Previously, any zero weight triggered an error. Negative weights are still rejected.

surveycore 0.6.0

Breaking changes

• survey_srs class and as_survey_srs() constructor have been removed. SRS designs are now created via as_survey() with no ids or strata — this produces a survey_taylor with no cluster/strata structure. All estimates are numerically identical.

New features

• get_diffs() estimates treatment effects (differences from a reference group) via survey-weighted regression. Supports bivariate and multivariate models, Gaussian and non-Gaussian families, and optional subgroup analysis. Two estimation paths: direct coefficients for simple models, and marginaleffects::avg_slopes() / avg_predictions() for models with covariates or non-Gaussian AMEs. Returns a survey_diffs tibble with optional mean, pct_change, n_weighted columns, significance stars, and p-value adjustment. marginaleffects moved from Suggests to Imports.

• as_survey() now supports multi-column FPC for multi-stage designs (e.g., fpc = c(fpc_stage1, fpc_stage2)). Each FPC column corresponds to one ID stage. Per-stage FPC is validated for NAs, non-positive values, and within-cluster constancy.

• print() for survey_taylor now displays per-stage FPC bullets for multi-stage designs (e.g., FPC (stage 1): fpc, FPC (stage 2): fpc2).

Bug fixes

• SRS variance estimation now uses Taylor (HT) linearization via .build_cluster_matrices(), correct for any weight structure. Previously used unweighted sample variance which was incorrect for non-proportional weights.

• survey_glm() now correctly indexes weights when na.action = na.omit drops non-contiguous rows.

• get_freqs() now routes survey_nonprob designs through the Horvitz-Thompson variance path, consistent with the other five analysis functions.

• as_survey_twophase() now accepts survey_replicate and SRS survey_taylor objects as the phase-1 design (previously restricted to stratified/clustered survey_taylor only).

• as_survey() SRS fallback downgraded from warning to message.

Internal infrastructure

• .build_cluster_matrices() extracts multi-stage cluster, strata, and FPC matrix construction into a shared helper, used across the Taylor variance engine, analysis cell estimators, and GLM sandwich variance.

surveycore 0.5.0

Breaking changes

• as_survey_replicate() replaces as_survey_repweights(). The constructor name now matches the underlying survey_replicate class.

• survey_nonprob and as_survey_nonprob() replace survey_calibrated and as_survey_calibrated(). “Calibrated” implies a post-processing step on a probability sample; nonprob accurately reflects the design type.

• survey_srs and as_survey_srs() have been removed. SRS designs are now created via as_survey() with no ids or strata — this produces a survey_taylor with no cluster/strata structure. All estimates are numerically identical. Print output now says “Taylor series linearization” instead of “simple random sample”.

• Single-row data frames are now rejected at construction time (previously a warning). This matches survey::svydesign() behavior.

• The positional setter form set_var_label(svy, age, "label") has been removed. Use the named form set_var_label(svy, age = "label") instead.

• extract_var_label(), extract_question_preface(), and extract_var_note() now return a named character vector. extract_var_label(svy, age) now returns c(age = "Age in years") rather than "Age in years".

• extract_val_labels() now returns a named list. extract_val_labels(svy, sex) now returns list(sex = c(Male = 1L, Female = 2L)) rather than c(Male = 1L, Female = 2L).

• set_variable_labels(), set_value_labels(), set_question_prefaces(), and set_variable_notes() have been removed. Use set_var_label(), set_val_labels(), set_question_preface(), and set_var_note() respectively — all four now accept multiple variables via named ....

New features

• set_universe() and extract_universe() set and retrieve universe (eligibility) annotations for survey variables.

• set_missing_codes() and extract_missing_codes() set and retrieve missing value code vectors for survey variables.

• extract_metadata() returns all metadata fields (variable_label, value_labels, question_preface, note, universe, missing_codes, transformations) for one or more variables as a named list.

Enhancements

• All setter functions now support three call conventions: named ... (e.g., set_var_label(svy, age = "Age in years")), a single named vector/list in ..., or explicit variable = / content-argument pairs. All setters also now work on plain data.frames.

• All extractor functions accept multiple variables via ..., support three output formats ("named_vector", "list", "data_frame"), and accept a fill argument to include variables with no metadata in the output.

surveycore 0.4.0

New features

• survey_glm() fits survey-weighted generalized linear models for all four design classes (survey_taylor, survey_replicate, survey_twophase, survey_nonprob); returns a survey_glm_fit object with design-based (Binder 1983 sandwich) standard errors and degrees of freedom.

• clean() converts a survey_glm_fit to a tidy survey_glm_tidy tibble with one row per coefficient, design-based confidence intervals, structured metadata, and optional reference rows for factor predictors.

• survey_glm_fit objects support 20 S3 methods: print(), summary(), coef(), vcov(), predict(), fitted(), residuals(), confint(), formula(), terms(), model.matrix(), model.frame(), deviance(), df.residual(), nobs(), hatvalues(), logLik(), AIC(), BIC(), and update().

• survey_glm_fit integrates with the marginaleffects package; when marginaleffects is installed, avg_slopes(), avg_predictions(), and the full marginaleffects API work directly on survey_glm_fit objects.

• broom::tidy() is supported for survey_glm_fit objects via a shim that delegates to clean().

• as_survey_rep() has been renamed to as_survey_replicate() to avoid a namespace clash with the srvyr package.

Bug fixes

• as_survey_twophase() variance estimation (method = "approx" and "full") now uses the correct PSU-level Phase 2 stratum sampling fraction instead of a row-level fraction, resolving an approximately 2× variance underestimation.

surveycore 0.3.3

New features

• print() methods for all four survey design classes (survey_taylor, survey_replicate, survey_twophase, survey_nonprob) now display a Domain: <n> of <N> rows line when surveytidy::filter() has been applied. The line appears after the sample size line and before the Groups: line. For two-phase designs, domain counts reflect Phase 2 rows only.

surveycore 0.3.0

New features

• names() now works on survey design objects, returning the column names of the underlying data frame. This enables IDE column-name autocomplete in RStudio and Positron when piping into analysis functions (e.g., design |> get_means().

surveycore 0.2.0

New features

• get_freqs() computes weighted frequency tables for categorical survey variables across all five design types, with domain estimation, value-label support, and AAPOR small-cell warnings.

• get_means() returns survey-weighted means with design-correct standard errors for all five design types, including grouped and domain estimation.

• get_totals() returns survey-weighted population totals (and population size when called without x) for all five design types.

• get_corr() computes survey-weighted Pearson correlation using the delta-method variance approach, with optional group parameter for per-group correlations and Fisher Z confidence intervals.

• get_quantiles() estimates survey-weighted quantiles using the Woodruff

  1952. linearization method; supports multiple probs in a single call and five CI interval methods.

• get_ratios() estimates survey-weighted ratios (numerator total / denominator total) with design-correct SEs via the delta method (Taylor, SRS, calibrated, two-phase) or direct per-replicate computation (replicate designs).

• All six analysis functions gain a decimals argument to round numeric output columns to a fixed number of decimal places.

• na.rm = FALSE now includes rows where a grouping variable is NA as a separate group row in all six analysis functions’ output.

• infer_question_prefaces() auto-detects shared battery prefaces from variable labels using separator-based and longest-common-prefix detection.

• survey_weighting_history() returns the weighting history stored in a survey design object’s metadata; as_survey(), as_survey_replicate(), and as_survey_nonprob() now promote "weighting_history" attributes from the input data frame automatically.

• Two-phase variance estimation (as_survey_twophase()) is now fully supported in get_means() and get_totals(), using the "full", "approx", and "simple" methods vendored from the survey package.

Bug fixes

• get_freqs() no longer crashes when the group variable contains NA values.

• get_freqs() now outputs pct as a proportion (0–1) rather than a percentage (0–100); se and se_srs are on the same scale.

surveycore 0.1.0

New features

• as_survey() creates survey_taylor objects with a tidy-select interface (ids, weights, strata, fpc, probs); supports Taylor linearization for stratified, clustered, and SRS designs.

• as_survey_replicate() creates survey_replicate objects; supports BRR, Fay BRR, JK1, JK2, JKn, bootstrap, ACS, and successive-difference replicate schemes.

• as_survey_twophase() creates survey_twophase objects; supports “full”, “approx”, and “simple” two-phase variance estimation methods.

• update_design() modifies design variables on an existing survey object without reconstructing from scratch; respects validate = TRUE/FALSE.

• get_means() returns a weighted mean and standard error via Taylor linearization or replicate weights; respects getOption("survey.lonely.psu") for single-PSU strata.

• get_totals() returns a weighted total and standard error using the same dispatch as get_means().

• Metadata setters: set_var_label(), set_variable_labels(), set_val_labels(), set_value_labels(), set_question_preface(), set_question_prefaces(), set_var_note(), set_variable_notes(). Single-variable setters automatically import haven "label" / "labels" attributes from the data frame column.

• Metadata extractors: extract_var_label(), extract_val_labels(), extract_question_preface(), extract_var_note().

• Conversion utilities: as_svydesign(), from_svydesign(), as_tbl_svy(), from_tbl_svy() — round-trip conversion between surveycore objects, survey::svydesign / survey::svrepdesign, and srvyr::tbl_svy.

• print() and summary() S7 methods for all survey design classes display design type, sample size, and a tibble-style data preview.

Internal infrastructure

• S7 class hierarchy: abstract survey_base → survey_taylor, survey_replicate, survey_twophase; survey_metadata for label storage.

• Three-layer validation: S7 structural validators, Layer 2 input validators, Layer 3 constructor validators; all errors use typed class= for programmatic handling.

• Variance estimation vendored from the survey package (Thomas Lumley, GPL-2/GPL-3) — see VENDORED.md for full attribution.