Release v0.7.0 · JDenn0514/surveycore

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.

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.

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.

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_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().

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.

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).