# surveycore 0.8.3

## CRAN preparation

* Resubmission addressing CRAN feedback on the 0.8.2 submission. Four
  changes: (1) Added DOI references to the package `Description`
  (Lumley 2004 for variance estimation; Mannan 2025 for weighted
  polychoric/polyserial correlation). (2) Uncommented three
  `as_survey()` calls in the `@examples` blocks of `anes_2024`,
  `gss_2024`, and `pew_npors_2025`, so they now run during
  `R CMD check`. The ANES and GSS examples (and the corresponding
  prose `@details` sketches) also gained `nest = TRUE`, which is
  required by those designs and silences a previously-emitted
  warning. (3) Replaced the `\section{Tidy-select}` `\preformatted{}`
  code sketch in `as_survey()` with prose, and added two runnable
  demonstrations to the `@examples` block (`c()` for multi-stage IDs
  on a synthetic frame, and `starts_with()` for tidyselect-helper
  weights on `gss_2024`). (4) Replaced `globalenv()` with `baseenv()`
  as the formula environment in `survey_glm()` to comply with CRAN
  policy on `.GlobalEnv` modification.

# surveycore 0.8.2

## CRAN preparation

* Resubmission addressing CRAN feedback on the 0.8.1 submission. Tagged
  numerical oracle and integration test files (comparisons against
  `survey`, `marginaleffects` integration, polychoric/polyserial MLE,
  vendored saddlepoint parity, and two-phase variance parity) with
  `skip_on_cran()`. Test runtime under `R CMD check --as-cran` drops
  from ~11 minutes to under 1 minute. The skipped tests continue to
  run on every push in CI and locally with `devtools::test()`.
* Single-quoted `'surveyverse'` in `Description` to match the
  convention used for other proper nouns (`'S7'`, `'tidyselect'`,
  `'haven'`) and silence the spell-checker NOTE.

# surveycore 0.8.1

## CRAN preparation

* Added Thomas Lumley to `Authors@R` as `[ctb, cph]` for the variance
  estimation code vendored from the `survey` package (R/variance-taylor.R,
  R/variance-replicate.R, R/variance-twophase.R,
  R/variance-vendored-saddlepoint.R). Vendoring is documented in
  `VENDORED.md`.
* Reworded the closing sentence of the package `Description` for grammatical
  completeness ("Automatically preserves..." instead of "Automatic
  preservation of...").
* Bumped `inst/CITATION` to track the upcoming release version.
* Removed the `\url{}` wrapper around `electionstudies.org` in the
  `anes_2024` data documentation. The URL is preserved as plain prose; the
  ANES homepage 403's automated requests, which previously triggered a
  `urlchecker::url_check()` failure under `R CMD check --as-cran`.

# surveycore 0.8.0

## Breaking changes

* Constructing a `survey_collection` from member surveys with divergent `@groups` now errors `surveycore_error_collection_group_divergent`. Previously, a mixed-grouping collection would dispatch analysis functions per-survey and stitch a patchwork of grouped and ungrouped rows together with `bind_rows()` — violating the pseudo-data.frame mental model. All members must either share `@groups` or the caller must supply `group =` explicitly.
* `as_survey_collection()`'s `.on_missing` argument has been replaced by `.if_missing_var`, and the previously silent no-op behaviour is fixed. `.if_missing_var` is now stored on the returned collection's `@if_missing_var` property and is honoured (rather than ignored) by every dispatched `get_*()`. Callers using the old name will see R's positional-argument-mismatch error.
* The `.on_missing` named-only argument on every collection-dispatching `get_*()` (`get_means()`, `get_totals()`, `get_freqs()`, `get_ratios()`, `get_diffs()`, `get_corr()`, `get_variance()`, `get_quantiles()`, `get_covariance()`, `get_t_test()`, `get_pairwise()`) has been renamed to `.if_missing_var`. The default flips from `"error"` to `NULL`; `NULL` resolves to the collection's stored `@if_missing_var` property, while a non-`NULL` value overrides it for that call. The `.id` argument similarly defaults to `NULL` and resolves to the collection's stored `@id`. Callers passing `.on_missing = ...` will silently have the value flow into `...` (no behaviour change at the analysis layer); update to `.if_missing_var = ...` to restore intent.

## New features

### `survey_collection` per-call dispatch defaults

* `survey_collection` gains two new properties:
  - `@id` (`character(1)`, default `".survey"`) — column name `.dispatch_over_collection()` uses when an analysis function is dispatched across the collection without an explicit per-call `.id`. Validated via the new shared helper; the existing `surveycore_error_collection_invalid_id` class fires on bad input.
  - `@if_missing_var` (`character(1)`, default `"error"`, must be one of `c("error", "skip")`) — controls how dispatched `get_*()` calls behave when a member survey is missing a requested variable. Validated via the new helper; raises the new `surveycore_error_collection_invalid_if_missing_var` error class on bad input.
* New exported setters `set_collection_id(x, id)` and `set_collection_if_missing_var(x, if_missing_var)` mutate the corresponding property and return the collection invisibly. Both validate via the same shared helpers; both raise `surveycore_error_not_survey_collection` on non-collection input.
* `add_survey()` and `remove_survey()` now propagate the source collection's `@id` and `@if_missing_var` onto the returned collection.
* `print(survey_collection)` renders `id:` and `if_missing_var:` lines on every print, regardless of whether they hold the default values.
* `.dispatch_over_collection()` resolves both `.id` and `.if_missing_var` via two-tier precedence: a non-`NULL` value at the analysis-function call site beats the value stored on the collection's property. The `surveycore_error_collection_id_collision` hint additionally surfaces `set_collection_id()` as a fix path when the collision was triggered by the stored `@id`.

### Uniform grouping on `survey_collection`

* `survey_collection` gains a `@groups` property (`character(0)` by default). Every member survey's `@groups` is asserted `identical()` to the collection's value by the class validator — a uniform-grouping invariant that guarantees dispatched `get_*()` results share a single grouping structure.
* `as_survey_collection()` gains a `group =` argument that accepts tidy-select column names (bare, `c()`, `all_of()`). Missing or empty-resolved `group =` (including `NULL`, `character(0)`, `c()`, `all_of(character(0))`) adopts the members' uniform `@groups` or errors on divergence; a supplied non-empty `group =` overrides any pre-existing member `@groups` and emits a typed `surveycore_warning_collection_group_overridden` per divergent member.
* `add_survey()` and `remove_survey()` now preserve `coll@groups` across mutation: a grouped collection propagates its `@groups` onto any empty-grouped new member and errors on divergent-grouped members (`surveycore_error_collection_group_conflict`); removal keeps the collection-level grouping.

### Polychoric and polyserial correlation via `get_corr(method = ...)`

* `get_corr()` gains a `method = "pearson"` argument. Setting `method = "polychoric"` fits a weighted two-step MLE for the correlation between two ordinal variables under a bivariate-normal latent model (Olsson 1979; Mannan 2025); `method = "polyserial"` fits the analogous MLE for one ordinal + one continuous variable (Cox 1974). Auto-detection of the ordinal / continuous side is handled internally; no new user-facing argument is required. Confidence intervals are constructed on the Fisher-z scale and back-transformed to `[-1, 1]`. Variance is design-based: Taylor linearization via a perturbation-based influence function on `survey_taylor`, and a full per-replicate re-fit of both thresholds and `rho` on `survey_replicate`. For `method != "pearson"`, `df = NA_integer_` and `statistic` is the z-scale Wald statistic referred to a standard normal distribution. `meta(result)$bivariate_normal_cdf` is `"pbivnorm"`, and `meta(result)$n_failed_replicates_total` carries the total count of non-converged replicates when the replicate path observed any. Agreement with `polycor::polychor()` / `polycor::polyserial()` on equal-weight fixtures is within `1e-4`.
* New package Import: `pbivnorm` (>= 0.6.0), used as the bivariate-normal CDF for the polychoric / polyserial likelihood.
* Fourteen new typed error / warning classes (PC-1 through PC-14) surface ordinal-type, optimizer, sparse-cell, boundary, and replicate-convergence conditions — see `plans/error-messages.md` for the full list.

## New functions

* `get_variance()` computes design-based finite-population variance estimates for one or more numeric variables in a survey design, matching `survey::svyvar()` at tolerance `1e-10` on point estimates and `1e-8` on SEs. Returns a `survey_variance` tibble with point estimate, SE, CI, CV, MOE, design effect (`deff`), and cell sizes. Supports grouping (via `group =` and `group_by()`), per-variable `na_handling = "pairwise"` (default) or `"listwise"`, `name_style = "broom"` renaming, and column-level `label` attributes for downstream gt integration. Dispatches over `survey_taylor`, `survey_replicate`, `survey_twophase`, `survey_nonprob`, and `survey_collection` designs.
* `get_covariance()` computes design-based finite-population covariance estimates for all unordered pairs drawn from one or more numeric variables in a survey design, matching the off-diagonal entries of `survey::svyvar()` at tolerance `1e-10` on point estimates and `1e-8` on SEs. Returns a `survey_covariance` tibble with covariance, SE, CI, CV, MOE, design effect (`deff`), and pairwise cell sizes. Pearson-only, pairwise-complete NA handling. Supports grouping (via `group =` and `group_by()`), `redundant = TRUE` to include both `(x, y)` and `(y, x)` orderings, `diagonal = TRUE` to include `(x, x)` self-pairs (which equal `get_variance(x)` exactly at `1e-10`), `name_style = "broom"` renaming, and column-level `label` attributes for downstream gt integration. Dispatches over `survey_taylor`, `survey_replicate`, `survey_twophase`, `survey_nonprob`, and `survey_collection` designs.

## New warning classes

* `surveycore_warning_variance_all_na` — fired when every row of the active domain is `NA` on the focal variable.
* `surveycore_warning_variance_insufficient_n` — fired when the focal variable has fewer than two non-`NA` observations in the active domain (variance is undefined).
* `surveycore_warning_covariance_all_na` — fired when every row of the active domain is `NA` on at least one variable in the pair.
* `surveycore_warning_covariance_insufficient_n` — fired when a pair has fewer than two pairwise-complete observations in the active domain (covariance is undefined).
* `surveycore_warning_covariance_non_numeric` — fired when one or more variables passed via `x` are non-numeric and silently dropped from the pair list.

# surveycore 0.7.1

## Documentation

* Trimmed the `Getting Started` vignette to remove dependencies on the sibling
  `surveytidy` package, which is not yet on CRAN. The correlation and ratio
  examples now clean data via `dplyr::filter()` on the underlying data frame
  before constructing the survey object. The standalone "Using surveytidy"
  section has been removed; those workflows are documented in the `surveytidy`
  package itself.

# surveycore 0.7.0

## Breaking changes

* `get_anova()`'s first argument is now `object` and dispatches on class. The former `model2` positional argument has been removed — `get_anova(fit1, fit2)` must now be written `get_anova(list(fit1, fit2))`. The S3 `anova(fit1, fit2)` interface is unchanged.

## New functions

### Design-based group comparisons

* `get_t_test()` performs a design-based two-sample t-test comparing group means for a numeric outcome across two levels of a `by` variable. Returns a `survey_t_test` tibble with estimate, per-group means and cell sizes, CI, t-statistic, df, p-value, and significance stars. Supports optional stratification via `group` (one row per stratum) and matches `survey::svyttest()` at tolerance 1e-10 for point estimates and test statistics.
* `get_pairwise()` computes all k(k−1)/2 pairwise t-tests across the levels of a factor, with multiple-comparison p-value adjustment via any `stats::p.adjust()` method (`"holm"` by default, or `"none"`). Adjustment is applied separately within each `group` stratum when stratified. Returns a `survey_pairwise` tibble with one row per pair.

### Design-based ANOVA

* `get_anova()` computes Rao-Scott design-based ANOVA for `survey_glm_fit` objects, supporting both Wald and LRT tests with F or Chi-squared reference distributions. Three dispatch branches:
  - `get_anova(<survey_glm_fit>)` — sequential term-by-term anova (matches `anova.svyglm()` semantics).
  - `get_anova(<list<survey_glm_fit>>)` — chained pairwise comparison across `k` nested fits, returning `k − 1` rows.
  - `get_anova(<survey_base>, formula = ...)` — fits the model internally via `survey_glm()` and runs sequential anova on the fit; extra `...` are forwarded to `survey_glm()`.
  Matches `survey::regTermTest()` at tolerance 1e-8 on statistics and 1e-6 on p-values.
* `anova(fit)` on a `survey_glm_fit` now dispatches to `get_anova()` via a registered S3 method.
* `plot()` on a `survey_glm_fit` produces a dot-and-whisker coefficient plot with design-based Wald confidence intervals.

### Select-all-that-apply (SATA) metadata

* `set_sata()` marks one or more variables on a survey design (or data frame) as select-all-that-apply. Accepts either tidy-select `...` or a `variable` character vector; setting `sata = FALSE` removes the flag. Idempotent on already-flagged variables.
* `extract_sata()` returns SATA status as a named logical vector (default), a list, or a data frame. `fill = FALSE` yields a dense view (unmarked variables reported as `FALSE`); `fill = NULL` returns only flagged variables.
* `classify_question_type()` classifies a set of requested variables into `"single"`, `"sata"`, or `"battery"` by grouping them on shared `question_preface` metadata and honoring per-variable SATA flags. Group numbers are assigned in order of first appearance. Warns when a lone SATA-flagged variable has no preface mate, or when a preface group has mixed SATA flags.

### Survey collections

* `survey_collection` is a new S7 container holding an ordered, uniquely-named list of `survey_base` objects — useful for wave-to-wave analyses, panel studies, or any workflow that compares estimates across multiple designs.
* `as_survey_collection()` constructs a collection from named (`wave1 = d1, wave2 = d2`) or bare (`d1, d2`) arguments; duplicate names are repaired by appending `_1`, `_2`, … with a warning showing the rename mapping.
* `add_survey()` and `remove_survey()` return new collections with surveys appended or removed; the original is unchanged.
* All nine `get_*()` analysis functions (`get_means()`, `get_totals()`, `get_freqs()`, `get_quantiles()`, `get_ratios()`, `get_corr()`, `get_diffs()`, `get_t_test()`, `get_pairwise()`) now dispatch over a `survey_collection`, iterating across surveys and returning a single combined tibble. Two new named-only control args on each function: `.id = ".survey"` names the identifier column, and `.on_missing = c("error", "skip")` controls behavior when a requested variable is absent from a survey. Regression functions (`survey_glm()`, `get_anova()`) do not support collection dispatch and raise an explicit error pointing users to `lapply()`.

## Other improvements

* `survey_glm()` gains a `quiet =` argument to suppress convergence warnings.
* `extract_*()` metadata functions now accept tidyselect helpers (`starts_with()`, `all_of()`, `any_of()`, `matches()`) in place of bare name lists.

## Bug fixes

* `get_diffs()` now correctly computes `pct_change` when `show_means = FALSE` is combined with grouped marginal effects and `show_pct_change = TRUE` (previously returned `NA`).

# 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.frame`s.

* 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.
