Changes in version 1.0.0 (2026-06-08)                  

First stable release

surveycore 1.0.0 is the first stable API release. The full set of survey
design types (Taylor series linearization, replicate weights, two-phase,
non-probability), variance estimators, analysis functions (means,
totals, frequencies, quantiles, ratios, correlations, regression,
t-tests, ANOVA, effective sample size), the metadata system, and
calibration-adjusted variance are now complete and stable.

New features

  - as_survey_nonprob() now accepts jackknife replicate schemes via type
    = "JK1", "JK2", and "JKn". All get_*() estimation functions dispatch
    to jackknife variance when a survey_nonprob design carries jackknife
    replicate weights. print() displays the replicate type and count for
    jackknife designs. (#133–#136)

  - survey_glm() now routes survey_nonprob designs that carry bootstrap
    replicate weights through the replicate-weight variance estimator,
    matching the behaviour of all other get_*() functions for such
    designs. (#138)

  - New dataset ca_api_2000: the 2000 California Academic Performance
    Index (API) survey, structured as a simple random sample from
    California schools. Used as a primary example for survey_taylor SRS
    designs throughout the documentation and vignettes. (#146)

Bug fixes

  - get_diffs(): fixed a regex metacharacter injection when variable
    names contained special characters (., *, ?, etc.), and fixed
    pct_change returning NA for some group configurations. (#145)

  - Fixed six print/dispatch/error bugs (B1–B6) uncovered during a
    documentation audit: print.survey_nonprob no longer errors on
    designs with no repweights set; dispatch errors now surface the
    correct class name in message text; three error classes that were
    raised but never registered have been registered. (#143)

Documentation

  - Corrected roxygen text, stale cross-references, contradictory @tags,
    and mismatched @section headings across 40+ R files (D1–D75).
    (#143–#144)

  - The non-probability estimation vignette section has been rewritten
    with a complete bootstrap variance example using as_survey_nonprob()
    with repweights.

                        Changes in version 0.9.0                        

New features

  - Raking oracle in test-calibration.R replaced with
    survey::calibrate(calfun = "raking") combined-matrix approach,
    matching the Architecture A variance formula.

  - as_survey() and as_survey_replicate() now validate the calibration =
    argument at construction time (errors CAL-15, CAL-16).

  - as_caldata() constructs a calibration data element suitable for
    assignment to design@calibration on survey_taylor and
    survey_replicate objects. Both classes now carry a @calibration
    property (default NULL) that will be consumed by the Taylor-series
    variance path in the upcoming PR 2.

  - get_corr() with method = "polychoric" or method = "polyserial" now
    supports survey_nonprob designs that supply bootstrap replicate
    weights via as_survey_nonprob(..., repweights = ...). Previously
    these designs always raised
    surveycore_error_polychoric_design_unsupported.

  - set_higher_is() and extract_higher_is() store and retrieve a
    direction-of-improvement attribute ("better" or "worse") for survey
    variables. The attribute is used by get_diffs(show_favorability =
    TRUE) to classify differences as favorable or backlash. Supports all
    three calling conventions (named ... args, named vector, variable =
    + direction =), data frames, and tidy-select.

  - set_reverse_coded() and extract_reverse_coded() store and retrieve a
    reverse-coded flag for survey variables. Supports tidy-select ...
    (bare names and selection helpers) and the variable =
    character-vector interface. Setting reverse_coded = FALSE removes
    the flag. Works on both survey design objects and plain data frames.

  - get_diffs() gains alpha and show_favorability arguments.
    show_favorability = TRUE appends favorable and backlash logical
    columns to the result. A difference is classified as favorable when
    it is statistically significant (p < alpha, default 0.05) and in the
    direction indicated by higher_is metadata set via set_higher_is().
    When no higher_is metadata is set, both columns are all FALSE. The
    adjusted p-value (when pval_adj is supplied) is used for
    classification.

  - as_survey_nonprob() gains replicate-weight (bootstrap) support via
    six new arguments: repweights, type, scale, rscales, mse, and
    reference_sample. When repweights is supplied, the constructor
    resolves and validates the replicate weight columns and stores them
    in @variables. A reference_sample property is also added to the
    survey_nonprob class for provenance tracking.

  - All get_*() estimation functions now dispatch to the
    replicate-weight variance engine when a survey_nonprob object has
    repweights set. When no repweights are supplied, a warning is
    emitted and the SRS approximation is used as a fallback. Degrees of
    freedom for survey_nonprob estimates always use the normal
    approximation (Inf).

  - print() for survey_nonprob now shows bootstrap replicate information
    (replicate count, type, and scale) when repweights are set.

                        Changes in version 0.8.4                        

New features

  - get_effective_n() computes the effective sample size of a survey
    design using the Kish (1965) weight approximation (method = "kish")
    or the full design effect for a specified variable (method =
    "deff"). Supports all design types and survey_collection.

                 Changes in version 0.8.3 (2026-05-05)                  

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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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).

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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().

                        Changes in version 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.

                        Changes in version 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.