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Changelog

Source: NEWS.md

spicy (development version)

spicy 0.14.1

  • Three tests compare their floating-point results with a tolerance, so that they pass under an alternative BLAS such as MKL. No change to the code.

spicy 0.14.0

CRAN release: 2026-10-31

This release adds table_multiresponse() for “check all that apply” questions and makes code_book() a codebook you can deliver, as an object, an Excel workbook, or a PDF. The descriptive tables share one contract for labels, p-values, notes, and machine outputs, and come out ready to publish: decked heads, the minus sign in Typst and LaTeX, decimals per variable, and notes you can replace, extend, or relabel. Some numbers change: table_categorical() computes its percentages on the observed values and picks its test per variable. The tables show column names unless labels = TRUE, and their machine outputs take the broom names.

Breaking changes

Percentages and counts

  • table_categorical() computes its percentages on the observed values: the (Missing) row shows its count and no percentage. percent_base = "total" gives back the percentages of 0.13.0.

  • A variable never observed in a group of by shows a dash there, not missing values, and the group leaves its Total and test: “No data for X in G.”, or “No data for X.” without by (#11).

Tests and effect sizes

  • table_categorical() defaults to test = "auto": chi-squared when all expected counts are 5 or more or weighted counts are not whole (with a warning), else Fisher’s exact test; "chisq" restores 0.13.0.

  • effect_size takes "eta2", "epsilon2", "cohens_d", and "hedges_g", as es_type does; the old values ("eta_sq", "d", …) and TRUE or FALSE (use "auto", "none") are errors.

  • table_categorical() and spicy_style() rename v_digits to effect_size_digits (v_digits is a spicy_defunct error), and a style’s value now reaches the association measure and the SMD.

Labels and names

  • The descriptive tables show column names by default: labels = TRUE or options(spicy.var_labels = TRUE) reads the label attribute. The regression tables print as before but refuse labels = NULL.

  • A labeled by heads its groups with their value labels ([1] Women n, not 1 n) in every table, as do the levels of a labeled select in table_outcome().

  • The descriptive tables refuse a level read as their (Missing) row or Total margin, and table_outcome() a variable shown as “Overall”: rename it, or relabel the row with options(spicy.labels).

  • An unknown name in labels, note_labels, a named digits, or a show_columns list warns (class spicy_unknown_name) and is left out, where some tables refused it.

Machine outputs

  • tidy() of table_categorical_svy() reports proportion, between 0 and 1, with its interval on that scale and in table order (it gave the percentage); its "long" output was its wide frame.

  • The long outputs, tidy(), and glance() take the broom names (see ?spicy): test_type, statistic, df, df.residual, and p.value (were statistic_type, chi2, df1, df2, p).

  • The effect size is es_type, es_value, es_ci_lower, and es_ci_upper (were effect_size_type, effect_size, assoc_*, ci_lower, ci_upper), and the SMD smd_value (smd).

  • The sizes are nobs, weighted_nobs, and weighted_n (were n_total, n_obs, or n, weighted.nobs, weighted.n), and the fit r.squared and adj.r.squared (r2, adj_r2).

  • tidy() gives a mean or coefficient, its standard error, and its interval as estimate, std.error, conf.low, and conf.high; the long outputs keep mean, se, ci_lower, and ci_upper.

  • variable holds the column name and label the label; tidy() and glance() called variable outcome in table_categorical(), table_continuous(), and table_continuous_lm().

  • output = "data.frame" and as.data.frame() give the printed table with its numbers: the keys (variable, label, level, group, .row_role), then the columns as printed (95% CI LL).

  • That frame splits a cell of several numbers (q1, q3). It was the long frame in table_continuous(), its twin, and table_outcome(), text in table_regression(), and levels in table_categorical().

  • table_categorical() had no label column and gave the label in outcome and in the variable of "long"; the Chi2 of its old data frame is the statistic of "long".

  • The long outputs of table_categorical() and table_continuous() keep the effect-size and SMD columns, NA, when not requested; table_outcome() drops its always-NA SMD columns.

  • glance() of table_categorical() lists the variables in the order of select and gives each measure in es_type ("phi", "cramer_v"); tidy() keeps the Total margin (filter on total).

  • table_continuous_lm() names by in the attributes group_var and group_label; a t, z, or chi-squared has its own df and an empty df.residual, and glance() gives the test the table shows.

  • Every table carries its whole note in attr(x, "note"), on the printed object and on the frames of output = "data.frame" and "long"; the missing_note attribute is now note.

  • as_structured() reads contract version 4 and refuses a view built by an older spicy; the format_spec of table_categorical() says effect_size_digits (was v_digits).

Headers and columns

  • The continuous tables name the median Mdn in headers and data frames (Mdn [Q1, Q3], Mdn 95% CI LL; was Med), and print Median and Mean under jama and lancet: a script indexing Med must change.

  • In French, the effect size of table_continuous() is headed Taille d'effet (was ES, which now heads the standard errors of the regression tables).

  • table_continuous_lm() with a categorical by sets M over the group labels and heads the difference Δ in every output, the note giving its order (Δ = B − A.), where it printed Δ (B - A).

  • table_categorical() heads one association measure with its symbol (V, φ, τb, γ), also in its data frame, and its bounds 95% CI LL and 95% CI UL; the header_ci_* keys of spicy.labels are gone.

Order of groups and levels

  • The tables sort a character by in the C locale (capitals first, alike on every machine), put FALSE first in a logical by, and sort a numeric one ascending, where they took the order of appearance.

  • A labeled by follows its value labels, or its codes when one is unlabeled. table_continuous_lm() sorted text in the machine’s locale, which chose its reference group.

  • The levels in table_categorical() and its survey twin, the groupings of table_outcome(), and the values of a character variable in freq() follow the same order.

inline()

  • inline() selects a group of by with model in every table, inline(tbl, bmi, model = "Female") (column = "emmean" for a mean of table_continuous_lm()); level is a spicy_defunct error there.

  • inline() cites a statistic of the variable (p, test, effect size, SMD) without model (the categorical tables also take a group), and in French picks an unnamed model by "Modèle 1", not "Model 1".

Notes

  • Every table note is one paragraph, opening with the lead-in of the style (“Note.” by default): the data, the missing values, the tests, the definitions. The stars legend follows on its own line (#11).

  • The descriptive notes open with the sample size, unweighted (“N = 159: Women (n = 80), Men (n = 79).”), name the test behind the p-values (“P-values:”, was “Group comparison:”), and flag weighting.

  • By default, a note ends with the definitions of the abbreviations of its headers and fit rows (“AIC = Akaike information criterion”), leaving M, SD, SE, B, OR, p, and the like undefined.

  • The jama and lancet themes use no lead-in word: jama lists the abbreviations first (“Abbreviations: HR, hazard ratio.”), lancet at the end (“HR=hazard ratio.”), neither defining SD, SE, or CI.

  • The missing values of by are listed with the others (“Missing values removed: score (3), sex (2).”), and each association measure of table_categorical() gets a sentence (“Phi: smoking.”).

Input checks

  • The tables refuse, with one error, a by, weights, or cluster column in select, the outcome among predictors, and the covariates in select, which they crossed, dropped, or described as variables.

  • A position in select counts every column, by included; a position naming such a column is refused, and everything() and the other helpers skip it, as the survey twins skip the columns of the design.

  • A column not in the data is an error (class spicy_missing_column) that names it and the close name it may be a typo of; a name in a character exclude was ignored.

  • The tables refuse data with no rows, a by that no observation holds under drop_na = TRUE, a select with no column of the right type, and, in table_categorical(), a NaN weight.

  • The tables refuse a fractional digits or boot_n, p_digits = 0, an output path not ending in .xlsx or .docx or in no existing directory, and unknown ... arguments of table_regression_uv().

Regression tables

  • table_regression(nested = TRUE) with a robust or cluster-robust vcov tests each added block with a Wald test on that matrix, so the change statistics differ; the classical tests are unchanged.

  • table_regression() refuses a robust vcov for the extended mgcv families other than nb() (tw(), betar(), …), whose score is not the GLM score.

  • table_regression() refuses a cluster-robust type a class does not compute: all but "CR1" for survreg, gam, polr, and the like, all but "CR0" for coxph and rms. The note says CR1, not CL.

  • table_regression() and table_regression_uv() return the file path, invisibly, with output = "excel" or "word", and name an interval column with a point whatever decimal_mark (97.5% CI LL).

  • table_regression() words its p_adjust note for the reader (within each model), opens a Bayesian interval note with the model’s label, and prints a note string after the lead-in, above the stars legend.

Codebook and data

  • code_book() returns a spicy_codebook object, printed as the list of variables, instead of opening a DT widget; output writes the file, and filename and include_na are defunct.

  • In code_book(), values is a number of categories (default 100), an explicit NA level of a factor counts as missing, and difftime columns no longer list their values.

  • sochealth gains a multiple-response question, five logical info_* items after dentist_12m: later columns move five places, and table_categorical(sochealth) tabulates 18 variables, not 13.

New features

Multiple-response questions

  • New table_multiresponse(), experimental, tabulates a “check all that apply” question: one row per item with its count and the percentage of respondents who chose it; inline() cites its cells (#10, #11).

  • table_multiresponse() gives the percentages of respondents, of responses, or both (percent), a row for the base (“respondents who answered the question”) and one for those who chose no item.

  • With by, table_multiresponse() tests each item (Holm-adjusted by default) and runs a global test that allows for several answers per respondent, Rao-Scott corrected or bootstrapped.

Descriptive tables

  • table_categorical(), table_continuous(), table_outcome(), and the survey twins gain p_adjust: the note adds “Adjusted for 3 tests (Holm method).”, and the raw p-values stay as p_unadjusted.

  • table_categorical() gains p_value (#9): FALSE drops the test from every output but glance(), which keeps its columns NA; with assoc_measure = "none" the table is purely descriptive.

  • table_categorical() gains percent = "row", whose percentages sum to 100 across the groups; under by, its note and that of its survey twin say which percentages they show.

  • table_categorical(total_row = TRUE) closes each variable with a Total row, set apart by a thin rule, which under row percentages gives the base rate they are read against.

  • table_categorical() gains test = "fisher" (Monte Carlo for large tables), and for 2x2 tables "midp" (Fisher mid-p) and "n1" (Campbell’s N-1 chi-squared); the long output gains test_type.

  • In table_continuous(), table_continuous_svy(), and table_continuous_lm(), digits takes a named vector: one value per variable for the numbers on its scale, the others at the default.

Labels, notes, and styles

  • labels takes four forms in every table: FALSE, TRUE, a named vector (c(bmi = "Body mass index")), or the new var_labels(), which keeps the label attribute of the columns it does not name.

  • Every table function takes note, whose string replaces the general note, and note_add, which adds sentences at its end; each keeps the title it prints in attr(x, "title").

  • Every table function gains note_labels, the label a variable takes in the title and the notes, so labels can keep a whole question in the rows: note_labels = c(sex = "sex at birth").

  • spicy_style() gains note_label, the word opening a note (“Note.” by default, “Note:”, or ""), and abbreviations, which abbreviations a note defines (the theme’s rule, "all", or "none").

Outputs and engines

  • Typst and LaTeX output (LaTeX only for gt) and the PDF of code_book() write a negative number with the minus sign (U+2212); spicy_style(minus_sign = "ascii") keeps the hyphen.

  • The long output and tidy() mark added rows by keys (.row_role, .group_role, total), tidy() of a table taking by always has a group column, and every result inherits spicy_table.

  • tidy() of table_continuous() and table_outcome() gains weighted_n, median, q1, and q3, and glance() of table_categorical() gains smd_type and smd_value.

Regression tables

  • With labels = TRUE, the regression tables name the outcome, numeric terms, and factor headers by their label (“Linear regression: Body mass index”); levels and interactions keep their names.

  • table_regression() supports cumulative-link mixed models, ordinal::clmm() fits and glmmTMB::glmmTMB() fits of the ordinal family, with their thresholds and random effects.

  • table_regression(vcov = "CR0") gives cluster-robust standard errors for glmmTMB() fits (glmmTMB >= 1.1.15), clustered by default on the top grouping factor of the random effects.

Codebook

  • code_book() builds a codebook in three parts (header, variables, values), which output writes to Excel, Typst source, or a PDF (cover, variable sheets, index) through the Typst that Quarto bundles.

  • code_book() gains subtitle, authors, notes (with italics, bold, code, and links), source, range, decimal_mark, and PDF settings (font, font_size, colors, paper, …).

  • code_book() documents declared missing values (rows in values, a summary, declared_codes) and names each type in plain words, in English or French (categorical nominal, numeric, date, …).

French output

  • Under options(spicy.language = "fr"), the regression and continuous tables write French headers (ES, IC à 95 % over LI and LS, R² ajusté, Modèle 1); column names stay English.

  • table_regression() writes (réf.) in French (reference_label defaults to the label of the language) and translates the reference footer; a label you supply is printed as typed.

  • table_regression() writes in French the standard-error line of every model class (#7), the p-value line, the notes on adjustment, exponentiation, and standardization, and the Bayesian and GEE notes.

  • In French, table_regression() translates its random-effects test and nested models (modèles emboîtés), table_regression_uv() its title, and table_continuous_lm() and table_outcome() write ajusté sur.

Minor improvements and bug fixes

All tables

  • The table functions warn (class spicy_ignored_arg) when a typed argument has no effect, such as excel_path without output = "excel" or adjustment without covariates.

  • Errors name the function called, not a helper, word each argument check one way, and carry the classes listed in ?spicy; a named digits where one number is expected is refused, with a hint.

  • A column name given to a table function, or to the select of mean_n(), sum_n(), and count_n(), means that column even when an object of that name exists in the calling environment.

  • The notes name a variable as the rows and title do (not by its column name), join a list with semicolons when a label holds a comma, and no longer double a label’s own “?” (“Do you smoke?.”).

  • Labels that state a subtraction take the minus sign (Q3 − Q1, the SMD direction), a range in a note an en dash (12–30), the singular-fit note ±1, and Cramér’s V its accent in English.

  • The console no longer draws a rule above a row whose label holds “Total” (“Total score”), and the Excel files leave an empty cell empty rather than holding an empty text.

Rendering

  • The lead-in of a note is in italics in every output, and LaTeX output keeps the Greek letters and decimal alignment under all three engines, with Cohen’s f² in math.

  • LaTeX output of tinytable compiles when a note holds % or _, centers each spanner over its columns, and scales a table wider than the text block down to it, note included.

  • output = "gt" sets the title at the size of the table text (the descriptive tables used 125%), flush left in LaTeX, where a one-column header has no rule and a wide table is scaled down.

  • Typst output prints the note as text (a URL or the stars legend broke it), keeps words whole (“Fe-male”), aligns wrapped level labels, and widens or spans columns under long labels (#11).

inline()

  • inline() gives one message for a column the table does not carry (“No column with token”p” in this table.”), and says how to select a group with model when a variable has no levels.

  • inline() matches a label exactly before it reads a fit statistic (a term “N” cited the number of observations), and refuses a variable that matches two rows, with an address for each.

  • inline() cites the partial_chi2 of table_regression(), and refuses a table_continuous_lm() row of means without column, giving the address of a mean.

Categorical tables

  • table_categorical() titles a grouped table with the label of by (“Categorical table by Sex”), no longer fails when two variables share a label, and shows a column named twice in select once.

  • table_categorical() reads a list in levels_keep per variable (list(smoking = "Yes")), as its survey twin does, and its glance() leaves the association interval NA unless assoc_ci = TRUE.

  • table_categorical() and its survey twin show a dash, not a blank, in the test and association cells of a variable they cannot compare, and name no association measure for it in the note.

  • table_categorical() reports the rows a missing weight removed in its note, where it warned up to three times; it and table_continuous() no longer warn about options(spicy.rescale).

  • table_categorical(output = "gt") shows the note on the association measure and the SMD and gives each spanner its own id; output = "excel" draws the rule between variable blocks.

  • table_categorical_svy() gives a percentage of 0 or 100 the exact binomial interval on its unweighted count, whatever ci_method, and says so, where it printed a zero-width interval or a dash.

  • table_categorical_svy() without select tabulates only categorical columns; its note counts a declared missing value once and no longer says it removed what its (Missing) row shows.

  • table_categorical_svy() names its test only when one ran, in the note and glance(), whose nobs counts the whole sample under include_total = FALSE; its note spells “chi-squared”.

  • With labels = TRUE, the survey twins keep their labels when rows with a missing by are dropped, and table_categorical_svy() heads the bounds of a proportion 95% CI LL and 95% CI UL.

Continuous tables

  • table_continuous(), table_continuous_svy(), table_outcome(), and table_continuous_lm() show a dash in a comparison cell they cannot compute; a blank cell only means one that does not apply.

  • These tables draw no inference on a constant variable, nor a parametric one on groups each constant: the test, p, and effect size show a dash and warn (spicy_undefined_stat), not p = 1.000 or noise.

  • table_continuous() no longer warns when drop_na = TRUE removes rows with a missing by, which the note counts; an effect size printed with its interval is headed ES [95% CI].

  • With ci = FALSE, table_continuous(), its survey twin, and table_continuous_lm() render one header row, as do table_outcome() without intervals and table_categorical_svy() without by.

  • With p_value = FALSE, output = "long" keeps no test column in table_continuous(), table_outcome(), the survey twins, and table_continuous_lm() (default NULL); glance() keeps them NA.

  • table_continuous_lm() fits no model to an outcome with no variance and shows a dash for what an exact fit leaves undefined, and no longer stops when a level of by has no complete observation.

  • table_continuous_lm() lists its covariates by label, keeps its note in gt::gtsave(), leaves out the contrast column under contrast = "none", and accepts a bootstrap of 10 replicates.

  • table_continuous_svy() compares a numeric by as groups (it tested a slope), compares an ordered by of two levels, gives each comparison its own degrees of freedom, and warns about drop_na.

  • The note of table_outcome() names its marginal row as relabeled, no longer names a test that failed, and says each block compares the outcome “without controlling for the other variables”.

Regression tables

  • table_regression(nested = TRUE) divides the deviance difference by the dispersion for glm(), mgcv::gam(), and mgcv::bam() fits that estimate one, and keeps the mgcv test when df round under one (#6).

  • table_regression() computes the cluster-robust standard errors of mgcv::gam() and mgcv::bam() fits itself: only a gam() with a canonical link and a fixed dispersion had them right.

  • With a cluster-robust vcov, table_regression() uses that matrix for the joint tests (partial_chi2) and the standardized coefficients of lmer and lme fits, which were model-based.

  • table_regression() handles glmer() fits with a Gamma, inverse-Gaussian, or non-identity gaussian family, and no longer fails on them or on glmer.nb() fits once merDeriv is loaded.

  • table_regression() titles a glmer.nb() fit “Negative-binomial mixed-effects regression”, gives the null theta two decimals, and calls a non-identity gaussian null “gaussian regression”.

  • table_regression() calls the partial_chi2 of mixed fits a Wald test, labels the mgcv standard errors “Bayesian (mgcv posterior covariance)”, and words those of glm.nb() as those of a glm.

  • table_regression() names the link of a non-logit zero part, speaks of the interval in the exponentiation note only when shown, writes the cloglog formula in math signs, and names the classes it supports.

Other functions

  • cross_tab() sets < apart in its note (p < .001), heads its corner Valeurs in French, and refuses a string where a column is expected (cross_tab(df, "sex", smoking) tabulated “sex”).

  • freq() writes a large declared missing code as it is (100000, not 1e+05), and varlist() and code_book() keep only the selected columns of an sf object.

spicy 0.13.0

CRAN release: 2026-10-01

table_regression() now covers more than thirty model classes and gains a univariable screen, the summary tables get survey-design twins, six journal styles and a French output arrive, and declared missing values are honored package-wide. The walk-throughs live as articles at https://amaltawfik.github.io/spicy/.

Breaking changes

  • Declared missing values (na_values, na_range, tagged NAs) now count as missing in freq(), cross_tab(), the table_*() family, the row-wise helpers, and varlist() / code_book(), so numbers change for labelled survey data. The tables disclose the exclusion in a note. user_na = FALSE restores the previous behavior.

  • varlist(), vl(), and code_book() use one missing definition for N_distinct, N_valid, and NAs on labelled data.

  • cross_tab() counts observations at an explicit NA factor level as a regular row or column. freq() excludes them from n_valid and the valid percent.

  • freq() and cross_tab() replace styled with output. styled = FALSE becomes output = "data.frame", and styled now errors with the replacement.

  • cross_tab(output = "data.frame") returns a plain data.frame without metadata attributes. Read them from the default object, for example attr(cross_tab(...), "p_value").

  • freq() defaults to rescale = FALSE, matching cross_tab(). Use rescale = TRUE for the previous behavior.

  • freq(), table_categorical(), table_continuous(), and table_continuous_lm() no longer print when their result is assigned. freq() drops its unused ..., so unknown arguments error.

  • options(OutDec) no longer changes spicy’s output. Every number follows decimal_mark alone, or the style or language in force.

  • table_categorical() defaults to drop_na = FALSE, showing missing values as a "(Missing)" level. Its labels must be a named vector, and p_digits below 1 errors.

  • table_categorical(output = "long") names the association column effect_size and adds effect_size_type. Replace out[["Cramer's V"]] with out$effect_size.

  • table_categorical(output = "flextable") no longer writes a .docx when word_path is supplied. Use flextable::save_as_docx().

  • table_continuous_lm(output = "data.frame") names the effect-size interval bounds es_ci_lower / es_ci_upper, like the "long" output.

  • standardized = "smart" scales continuous inputs by 2 SD and leaves binary inputs unscaled. The rule was applied inverted since 0.12.0, so those betas change.

  • table_regression(exponentiate = TRUE) errors on links whose exponentiated coefficient is not a ratio (probit, cauchit, inverse, sqrt).

  • keep / drop no longer match the intercept row. show_intercept alone controls it.

  • align = "auto" is removed. Use "decimal" (the default), "center", or "right".

  • The show_fit_stats information criteria are lowercase tokens ("aic", "aicc", "bic"). show_fit_stats = character(0) errors. Use FALSE to suppress the block.

  • With several models, show_columns = "all_b" / "all_ame" drop the CI columns. Request atomic tokens to keep them.

  • A robust vcov that cannot be computed is now an error, and a cluster containing NA is refused. It used to warn and label the classical variance robust.

  • Weighted n and glance()’s weighted_nobs are NA for an unweighted glm(). They used to repeat n.

  • as_structured() describes each row in the body itself (body$.row_role, body$.indent, cell_status). The 0.12.0 row-index vectors are removed, version is 3, and a view built by an older spicy is refused.

  • tidy() labels AME rows estimate_type = "ame" (was "AME").

  • count_n() warns and returns NA when the selection resolves to no usable column. mean_n() and sum_n() with min_valid = 0 return NA for rows with no valid values.

  • copy_clipboard() arguments use snake_case. build_ascii_table() is no longer exported. Use spicy_print_table().

  • Association measures with detail = TRUE always include an se element. On degenerate tables, gamma_gk(), kendall_tau_b(), kendall_tau_c(), and uncertainty_coef() return NA with a classed warning instead of a spurious value. conf_level is validated everywhere.

  • The package ships a single vignette, Get started. The walk-throughs live as articles on the package site, at the same URLs, and vignette("<name>") no longer finds them.

New features

  • table_regression() supports more than thirty model classes beyond lm / glm: mixed effects (lmer, glmer, glmmTMB, lme, gls), GEE, Bayesian (stan_glm(), stan_glmer(), brm()), survival (coxph, survreg, cph, flexsurvreg), ordinal (polr, clm), multinomial (multinom, mlogit), two-part counts (zeroinfl, hurdle), fixest, estimatr, ivreg, tobit, rq, rlm, glm.nb, nls, gam, betareg, selection, rms, and the design-based svyglm, svyolr, and svycoxph. ?table_regression_models is the registry. A request a class cannot honor is refused with a classed error, never rendered as an empty column.

  • Each family renders with its own conventions. Mixed models report their random effects as a block of rows with SE and CI, the ICC, and a boundary-correct test of the random part. Ordinal models report their thresholds. Two-part models report every component. fixest and estimatr fits disclose their absorbed fixed effects as a Fixed effects: block. Bayesian fits report posterior medians, MAD SD, and credible intervals, with no p-values and a sampler-diagnostics guard.

  • New table_continuous_svy() and table_categorical_svy() summarize a survey design object, with every statistic computed by survey and the design degrees of freedom throughout. Design-based regressions (svyglm(), svyolr(), svycoxph()) report both counts and name their variance estimator in the note.

  • New table_outcome() summarizes one continuous outcome across several categorical variables, one block per grouping, with the group comparison and an Overall row.

  • New table_regression_uv() builds univariable screening tables for lm, glm, and coxph outcomes, one fit per predictor merged beside the multivariable model, with a per-predictor N column.

  • New inline() cites one table cell in Quarto or R Markdown text. The returned string is exactly the displayed cell, so a quoted number can never drift from the table.

  • New style argument on the four table families, and options(spicy.style = ) for a whole document: "jama", "nejm", "lancet", "annals", "apa", and "aer". A theme applies the rules its journal publishes, as defaults, so any argument you pass wins. spicy_style() builds a style by hand or from a theme.

  • options(spicy.language = "fr") prints table labels in French and brings French typography with it: a decimal comma and a leading zero on p-values (0,003). Machine outputs, column names, and messages stay in English. options(spicy.labels = ) overrides one label at a time, and spicy_labels() lists them.

  • New show_columns families "rmst" and "risk_diff" for coxph and survreg fits: covariate-adjusted differences in restricted mean survival time and in cumulative incidence, by g-computation with bootstrap inference, in single tables and in the univariable screen.

  • New show_columns token "n_events" shows event counts as events/N beside the estimates, for binomial outcomes and coxph fits.

  • Heteroskedasticity- and cluster-robust vcov across the supported classes, with each class’s field-standard backend. "CR1S" reproduces Stata’s regress, vce(cluster) exactly. What no backend supports is refused, never approximated.

  • ci_method = "profile" gives profile-likelihood CIs for glm, polr, and clm. ci_method = "boot_percentile" reports percentile CIs from the bootstrap replicates.

  • nested = TRUE works across the new classes with the correct test for each, and refuses hierarchies that are not comparable.

  • AME columns are available for many more classes, per outcome category for ordinal and multinomial models, and honor a robust vcov. broom::tidy() gains outcome_level for those rows.

  • table_categorical() and table_continuous() gain smd = TRUE, a standardized-mean-difference column, the balance diagnostic of a Table 1.

  • table_continuous() gains weights and rescale, under a documented convention that matches Stata’s [aweight] and survey::svyvar(). Group tests are refused under weights. table_continuous_lm() is the tool for that.

  • table_continuous() gains show_columns with median tokens ("med", "q1", "q3", "iqr", "med_iqr", "med_ci"), per variable via a named list. A variable shown as a median is tested as one.

  • select is optional in table_categorical(), and table_continuous() gains drop_na = FALSE.

  • as_structured() reads the descriptive tables too, and carries everything the printed table shows, with a per-row identity (.variable, .level, .row_role) that survives stacking.

  • Seven new articles on the package site: mixed-effects, GEE, multinomial, count and two-part, survival, ordinal regression tables, and categorical predictors.

Minor improvements and bug fixes

The first eight fixes change numbers that 0.12.0 reported.

  • kendall_tau_b() reported wrong standard errors, confidence intervals, and p-values in every release from 0.6.0 through 0.12.0. Point estimates were correct. assoc_measures() and cross_tab() were affected too.

  • Binomial models fitted with a cbind(successes, failures) response were refitted with squared weights by every internal refit. Bootstrap and jackknife inference, the default McFadden and Nagelkerke R², and standardized = "refit" were wrong for them. Fits with a 0/1, factor, or proportion-plus-weights response were never affected.

  • Average marginal effects use the fit’s prior weights, so AME values change for weighted fits.

  • Partial effect sizes are true Type-II tests. In models with interactions, main effects no longer depend on the factor coding.

  • ci_method = "profile" with a robust vcov defers to the vcov and warns.

  • table_categorical() computes the ordinal association measures in declared level order under drop_na = FALSE.

  • table_continuous_lm() reports correct estimates when by is an ordered factor and correct "balanced" adjusted means with an ordered-factor covariate. It pins treatment contrasts, so options(contrasts = ) no longer alters the results.

  • cross_tab() computes weighted totals from the unrounded table.

  • Under decimal_mark = "," every surface follows the mark. P-values keep their leading zero (0,018), and the star legend, the change statistics, and the association intervals read the comma.

  • nested = TRUE no longer reports a negative chi-square with a p-value when the models are passed largest-first.

  • cramer_v(), phi(), and contingency_coef() return NA with a classed warning on a zero margin, and somers_d(direction = "symmetric") returns 0 on equal concordant and discordant pairs.

  • The tau_c measure is labeled "Stuart's Tau-c" everywhere.

  • freq() keeps its label footer when NA-weight rows are dropped, warns when distinct codes merge under one label, and sorts labelled variables by code under sort = "name+".

  • cross_tab() reports excluded missing values in the table note, accepts logical weights like freq(), and no longer swallows the warnings of its association measures.

  • table_categorical() displays labelled columns as "[code] label" levels in every path, and keeps both the group and the margin when a by level is named "Total". Its machine outputs carry full-precision values and the documented Chi2 and df columns.

  • table_categorical(), table_continuous(), and table_continuous_lm() resolve by data-first, like tidyselect. A by with no level to tabulate is refused.

  • table_continuous() forms groups from a non-factor by in order of first appearance, and degrades per variable when a test fails on degenerate data.

  • table_continuous() and table_continuous_lm() label an interval with its own coverage (97.5% CI, not 98% CI).

  • table_continuous_lm() discloses robust and resampling SEs in the note, treats a value-labelled by as categorical, and degrades cleanly on degenerate fits.

  • output = "gt" tables keep their note when saved or printed non-interactively. gt and flextable outputs render in Quarto and R Markdown Word, PowerPoint, and PDF documents, where they silently disappeared. New as_flextable() returns the underlying flextable.

  • output = "gt" escapes a by level or model name carrying a quote, an angle bracket, or a backslash.

  • stars = TRUE marks the coefficients in every output, not just the console.

  • A cell whose statistic applies but has no number shows the console’s en dash in every rich output, instead of a blank.

  • A table_categorical(by = ) table carries its association note to every output. The descriptive gt and tinytable outputs also draw the title and the rule between variable blocks.

  • Factor levels are indented once, not twice, in the tinytable, Word, and Excel outputs.

  • Tables without a confidence-interval column lose their empty header strip, multi-line notes keep one disclosure per line, and Typst output no longer forces a column gutter under grouped headers.

  • Footer lines cite a model by its displayed label, not Model 1.

  • output = "excel" writes the significance stars, blank cells instead of #N/A, honors align, and sizes its columns to the text.

  • output = "clipboard" quotes cells that contain the delimiter, ships plain text instead of Excel formulas, and errors clearly on a system without a clipboard.

  • Console layout survives NA cells, empty cells, and wide characters (CJK, emoji).

  • table_regression(m1, m2) without list() errors helpfully, and an NA or colliding model name no longer crashes the table.

  • Factor rows follow levels() order instead of alphabetical, and factors fit with non-default contrasts (successive differences, sum-to-zero, Helmert) group under their parent variable.

  • A factor level containing : ("Part-time: 50-89%") stays inside its variable block. It used to be mistaken for an interaction term.

  • The statistic column header follows each model’s reference distribution (z or t).

  • Bootstrap, jackknife, and standardized = "refit" refits no longer leak the caller’s environment and work on factor() / log() / poly() formulas.

  • varlist() and code_book() render POSIXlt columns and difftime units, and show an explicit NA factor level as <NA>.

  • count_n() resolves select and exclude through the same tidyselect path as mean_n() / sum_n(), and errors clearly on an unusable count or special.

  • The tabulating and summarizing functions reject bit64::integer64 input with a classed error naming the fix.

  • Error messages quote values the same way on every platform, and the enum arguments raise classed errors naming the valid values.

  • copy_clipboard() re-emits backend messages and warnings as real R conditions.

  • An estimatr fit reports R² and adjusted R² by default like lm, discloses its absorbed fixed effects, and labels its model type after its se_type.

spicy 0.12.0

CRAN release: 2026-05-19

New features

  • New table_regression(): publication-ready coefficient summary for one or more fitted lm or glm models, side by side. APA Manual 7 formatting is the default. Highlights:

    • Robust variance: classical, HC, cluster-robust (CR) with Satterthwaite df, bootstrap, jackknife. Per-model vcov accepted for SE-comparison tables.
    • Standardization: refit, posthoc, basic, smart, pseudo (the last glm only).
    • Average marginal effects (AME) as separate columns; AME inference shares the coefficient’s variance estimator so B and AME are reported on the same inferential footing.
    • Partial effect sizes: f², η², ω² for lm (noncentral-F CIs); partial χ² for glm.
    • GLM response-scale reporting via exponentiate = TRUE, with family-appropriate labels (OR, IRR, HR, RR, MR, exp(B)) and optional profile-likelihood CIs (ci_method = "profile").
    • Multiplicity correction via p_adjust (any stats::p.adjust() method).
    • Hierarchical comparison via nested = TRUE (ΔR² / F-change for lm; LRT for glm).
    • Display controls: variable filtering, intercept and factor placement, reference-row styles, multi-model labels, stars, decimal mark, per-column digits.
    • Outputs: console, data.frame, long tibble, gt, flextable, tinytable, Excel, Word, clipboard. broom::tidy() and broom::glance() methods supported.

    See ?table_regression and vignette("table-regression").

  • table_continuous_lm() gains additive covariate adjustment via the new covariates argument. Two estimands for the per-group adjusted means: "proportional" (G-computation, default) and "balanced" (equal-weight synthetic grid). Under adjustment, f² and ω² become partial effect sizes; d and g raise an explanatory error. The auto-built footer documents the covariates and the estimand. See vignette("table-continuous-lm").

  • New exported as_structured() accessor returns a typed view of a table_regression() result for programmatic use: raw numerics, CI split into LL / UL columns, and a column-level format specification.

Breaking changes

  • code_book() no longer silently truncates the export filename to 120 characters. Very long titles now surface a clear OS-level error. Migration: shorten the title or pass an explicit filename = argument.

Bug fixes

  • table_categorical() no longer over-truncates a p-value in the interval (10^-p_digits, 0.001) when p_digits >= 4. Example: p = 0.000108 now correctly prints as ".0001" at p_digits = 4 (was "<.0001").
  • count_n(special = ...) returns a length-nrow(data) zero vector when no usable column survives the list-column filter, matching the documented contract and the count = ... branch (was numeric(0), which broke dplyr::mutate() pipelines).
  • lambda_gk() and goodman_kruskal_tau() emit spicy_undefined_stat and return a fully-NA result on rank-1 contingency tables (constant predicted variable), matching the existing pattern in gamma_gk(), kendall_tau_b(), somers_d(), and yule_q().
  • cross_tab() no longer silently overwrites a user’s y-variable level named "N", "Total" or "Values". The conflicting reserved column is auto-renamed with a numbered suffix and a single spicy_renamed_column warning is emitted.
  • broom::glance() on a spicy_continuous_lm_table keeps df.residual numeric, so Satterthwaite degrees of freedom from vcov = "CR2" / "CR3" are preserved verbatim instead of being truncated through as.integer().

Minor improvements

  • Console en-dash alignment: non-numeric placeholders (en-dash, “NA”) sit at the decimal-mark column instead of the integer- part column (APA Manual 7 §7.13). Integer cells in mixed- precision columns (n row alongside R²) keep their right- aligned placement.
  • R/ source is byte-pure ASCII (tools::showNonASCIIfile() reports zero hits package-wide).
  • openxlsx2::wb_add_border() calls now pass NULL on unused sides, preventing the default "thin" from being applied to all four sides of a cell when only one rule is intended.

spicy 0.11.0

CRAN release: 2026-05-04

New features

table_continuous_lm()

  • Cluster-robust SEs via cluster and four vcov choices ("CR0"–"CR3"), dispatched to clubSandwich with Satterthwaite df (clubSandwich in Suggests).
  • vcov = "bootstrap" (nonparametric or cluster) and vcov = "jackknife" (leave-one-out / leave-one-cluster-out) variance estimators in pure base R, controlled by boot_n.
  • Three new effect_size choices alongside "f2": Cohen’s "d", Hedges’ "g" (two-group only), Hays’ "omega2". New effect_size_ci adds noncentral t / F CIs rendered inline as 0.18 [0.07, 0.30].
  • HC* estimators delegate to sandwich::vcovHC(); rank-deficient fits return a clean rank-by-rank covariance.

Harmonization across the table family

  • Shared reporting vocabulary (decimal_mark, p_digits, align, named-labels) now spans cross_tab(), freq() and the three table_*() helpers, including APA-style p-value notation (<.001 / .045, no leading zero).
  • table_categorical()’s assoc_measure accepts a per-variable spec. When measures differ across rows the column collapses to "Effect size" and an APA-style Note. line documents the per-variable measure; phi on a non-2x2 errors.
  • All three table_*() functions gain as.data.frame(), tibble::as_tibble(), broom::tidy() and broom::glance() methods (broom in Suggests).

Quality and robustness

  • Classed conditions. Errors and warnings now carry stable classes (spicy_error / spicy_warning plus 11 leaf classes documented in ?spicy), so downstream code can dispatch via tryCatch() / withCallingHandlers() instead of matching message strings. rlang (>= 1.1.0) required.
  • Structured cli messages. Multi-line errors and warnings (vcov fallbacks, bootstrap/jackknife failures, padding migration, labels length mismatch) render as cli bullets.
  • Locale-deterministic ordering. Sorts in varlist(), freq(), cross_tab() and table_*() use method = "radix". Output is byte-stable across locales and platforms, matching Stata / SPSS guarantees.
  • Edge-case hardening. A new length-guarded sort helper makes varlist() / code_book() / cross_tab() / freq() survive zero-length or all-NA Date / POSIXct / character columns and factors with no observed levels.
  • Snapshot-locked rendering. tests/testthat/test-snapshots.R pins the exact console output of every spicy print method, so any unintended formatting drift surfaces as a PR diff.
  • API stability contract. ?spicy documents which exports are stable, stabilizing or internal. pkgdown reference groups exports via four @family tags.
  • Cross-software validation. All 13 association measures agree with PSPP 2.0 (CROSSTABS /STATISTICS=ALL, 65 / 65 statistics on four datasets); Cohen’s d and Hedges’ g noncentral CIs are tested numerically against effectsize::cohens_d() / effectsize::hedges_g() (tolerance = 1e-6); point-estimate formulas and asymptotic standard errors follow DescTools (Signorell et al.).

Improvements

  • cross_tab() warns when correct = TRUE is ignored on a non-2x2 sub-table, when weights contains NA, and notes statistics computed on a sub-table after empty rows / columns are pruned.
  • cross_tab() validates decimal_mark, p_digits and simulate_B up front; freq() validates decimal_mark and tightens digits to a non-negative integer.
  • A user category literally named "N" or "Total" is no longer mis-rendered as the totals row in cross_tab().
  • table_continuous_lm(output = "long") returns n, df1, df2 as integer columns; predictor_label preserved on the degenerate-model fallback path.
  • cramer_v() / phi() doc states the CI uses the Fisher z-transformation (point estimate and p-value identical to DescTools / SPSS).
  • uncertainty_coef() doc states entropy uses 0 log 0 = 0 (matching SPSS, PSPP, Stata, Cover & Thomas).

Bug fixes

  • label_from_names() raises actionable errors on duplicate or empty new column names; trims whitespace and preserves the input class.
  • table_continuous_lm(output = "data.frame") names contrast CI columns from ci_level (was hardcoded to 95 %).
  • The categorical-predictor global Wald F degrades to NA on a singular coefficient covariance submatrix.
  • The degenerate-table branch of cramer_v(), yule_q(), gamma_gk(), kendall_tau_b() and somers_d() respects detail: scalar NA_real_ by default, fully shaped spicy_assoc_detail when detail = TRUE.
  • uncertainty_coef() returns a finite estimate (was NaN) when a marginal is zero.
  • somers_d(direction = "symmetric") returns the harmonic mean of the two asymmetric values, matching SPSS / PSPP CROSSTABS.
  • print.spicy_assoc_detail() / print.spicy_assoc_table() use APA-strict <.001 / .045 notation, matching the rest of the package.
  • varlist() / code_book() honor factor_levels = "all" for haven_labelled columns: declared-but-unobserved labels appear in the Values summary.
  • copy_clipboard() rejects row.names.as.col vectors of length ≠ 1 and empty strings; accumulates all messages from clipr::write_clip() instead of overwriting.
  • mean_n() / sum_n() reject non-integer min_valid >= 1 and min_valid > ncol; their digits requires a non-negative integer.

Breaking changes

  • table_continuous_lm() and table_categorical() default to decimal-point alignment for numeric columns (align = "decimal"). Pass align = "auto" for the previous behavior.
  • build_ascii_table() / spicy_print_table(): padding switches from a string enum to a non-negative integer. Default 2L (was +5L); printed tables are roughly 40 % narrower. Migration: "compact" -> 0L, "normal" -> 2L, "wide" -> 4L.
  • table_categorical(assoc_measure = "auto") on a 2x2 table picks phi instead of cramer_v. Numeric value unchanged (|phi| = V on 2x2); only the column label changes.
  • freq() drops observations with NA weights (with a warning) instead of recoding them to zero. Aligns with cross_tab().
  • table_continuous_lm(output = "long") returns NA in es_type / es_value when effect_size = "none" (was "f2"), and renames sum_w to weighted_n.

spicy 0.10.0

CRAN release: 2026-04-27

New features

  • code_book() now accepts tidyselect-style variable selectors through ..., matching varlist() and vl().

  • code_book() gains a filename argument for the base name of CSV, Excel, and PDF exports. When NULL (the default), the filename is derived from title and falls back to "Codebook" when needed. Filenames are sanitized to portable ASCII consistently across platforms.

  • varlist() now summarizes matrix and array columns by their dimensions, and counts valid, missing, and distinct observations by rows.

  • freq() gains a factor_levels argument that mirrors varlist() and code_book(). With factor_levels = "all", declared-but-unobserved factor and labelled levels appear in the output with n = 0, matching SPSS FREQUENCIES; the default "observed" preserves the previous Stata tab-style behavior.

Improvements

  • varlist() now displays missing values as <NA> and <NaN> in the Values summary when include_na = TRUE, and quotes literal "NA", "NaN", and empty-string values so they cannot be confused with the missing markers.

  • varlist() now emits a column-named warning and marks the failing cell as <error: ...> when a column cannot be summarized, instead of silently writing "Invalid or unsupported format". Remaining columns are unaffected.

  • varlist() produces more precise Viewer titles for extraction, pipe, and literal get("name") expressions, while keeping ambiguous dynamic calls anonymous (vl: <data>).

  • code_book() now rejects partial-match names in ... (e.g. val = TRUE, tit = "x") that would otherwise be silently treated as tidyselect expressions, and surfaces varlist() selection errors directly.

  • freq() now resolves the weights argument via tidy-eval, so column references nested in compound expressions (e.g. weights = if (use_w) col else NULL) work as expected. Qualified expressions like weights = df2$w continue to take precedence over column lookup.

  • freq() validates digits, sort, weights, and the logical scalar arguments (valid, cum, rescale, styled) more strictly at the public boundary, with clearer error messages for non-finite values, NA, multi-element inputs, and non-numeric weight vectors.

  • freq() now documents the interaction of weights containing NA with rescale = TRUE (Stata pweight semantics) and the dropping of unused factor / labelled levels (Stata tab semantics, with code_book(factor_levels = "all") as the schema-style alternative).

Bug fixes

  • varlist() now displays labelled values in the same prefixed-label order for compact and values = TRUE summaries; previously the compact summary used data order.

  • varlist(values = TRUE) now deduplicates element types when summarizing list-columns. Previously list(1L, 2L, "a") produced "List(3): character, integer, integer"; now produces "List(3): character, integer".

  • include_na = TRUE now correctly appends <NA> markers for list-columns in both varlist() modes; previously it had no effect on this column type.

  • varlist() now validates column names up front and gives clearer errors for missing, empty, NA, or duplicate names.

  • varlist() now errors clearly when tidyselect expressions try to rename columns; ... is for selecting variables, not renaming.

  • freq(data, x, weights = NULL) now correctly treats the explicit NULL as “no weighting” instead of emitting a misleading "variable 'NULL' not found" error. Parameterized patterns like weights = if (use_w) wts else NULL are now supported.

  • print() for spicy_freq_table no longer crashes when the var_label attribute is NA_character_, numeric, or multi-element; the Label: line is silently skipped for any value that is not a single non-empty string.

  • freq() no longer surfaces the name of the ignored data vector in the printed footer when both data and x are passed as vectors. The footer now consistently shows the analyzed vector’s name.

spicy 0.9.0

CRAN release: 2026-04-20

Breaking changes

  • table_continuous() now enables inferential output by default when by is supplied. With a grouping variable, the p column from test is shown automatically (previous default hid it). This aligns the two table helpers: table_continuous() stays descriptive when by is absent, and reports the test p-value when by is supplied, matching table_continuous_lm()’s inferential default. To preserve the previous behavior, pass p_value = FALSE explicitly. statistic and effect_size remain FALSE by default and must still be enabled consciously.

  • varlist() now displays observed factor levels by default in Values, matching its role as a quick inspection of the current data. Use factor_levels = "all" to display unused factor levels as well, which was the previous default behavior and remains the default in code_book().

Minor improvements

  • code_book() gains a factor_levels argument. It defaults to "all" so exported codebooks continue to document all declared factor levels, including unused levels; use "observed" to mirror varlist() output.

  • freq() now prints the Freq. column as integers regardless of digits, which continues to control percentage precision. This matches the convention of SPSS, Stata, and SAS PROC FREQ for weighted counts and keeps the two numeric concepts (discrete counts vs. continuous percentages) visually distinct.

  • freq(..., styled = FALSE) now returns a genuinely plain data.frame with no spicy_freq_table rendering metadata clinging to it, so str(), dput(), and downstream programmatic use see only the tabulation columns. The metadata attributes (digits, data_name, var_name, var_label, class_name, n_total, n_valid, weighted, rescaled, weight_var) are now documented in @return and remain available on the invisibly returned spicy_freq_table object when styled = TRUE (the default).

  • table_continuous_lm() documentation now clarifies why p_value = TRUE and r2 = "r2" are the defaults, and robust-variance fallback warnings are now more explicit when a model matrix is singular.

Bug fixes

  • freq() now correctly resolves qualified weight expressions such as weights = other$w or weights = other[["w"]] even when the referenced column name also exists in data. Previously the bare-name fallback could silently pull the weight vector from the wrong data frame when column names collided.

  • freq() with sort and missing values now keeps the NA row at the end of the tabulation so the printed Cum. Percent and Cum. Valid Percent columns stay monotonic and match the Valid → Missing → Total display layout. Sorting previously could push the NA row between valid rows and make cumulative percentages appear to jump.

  • varlist() now preserves literal "NA" and empty-string values in the Values summary instead of removing them as if they were missing values.

  • varlist() now distinguishes actual NA values from NaN in the Values summary when include_na = TRUE.

  • varlist(values = TRUE) now preserves factor level order in the Values summary, matching the default compact factor display.

  • varlist() now validates values, tbl, and include_na up front and gives a clear error when one of them is not TRUE or FALSE.

spicy 0.8.0

CRAN release: 2026-04-10

New features

  • table_continuous_lm() adds APA-style bivariate linear-model tables for continuous outcomes. It acts as the model-based companion to table_continuous() for reporting fitted mean comparisons or slopes in an lm framework, with one predictor per model, model-based means for categorical predictors, optional case weights, classical or HC0-HC5 variance estimators, multiple output formats (ASCII, tinytable, gt, flextable, Excel, clipboard, and Word), output = "data.frame" for the wide raw table, output = "long" for the analytic long table, and configurable display of tests, confidence intervals, fit statistics, and effect sizes.

Minor improvements

  • Installed package vignettes now avoid embedding heavy HTML table and codebook widgets during CRAN builds, reducing package size while preserving rich pkgdown article rendering.

  • Website and vignette coverage now includes table_continuous_lm(), using the bundled sochealth data throughout and adding a dedicated article for model-based continuous summary tables.

  • table_continuous() and table_continuous_lm() now support dedicated display precision for effect-size columns, and table_continuous_lm() also supports separate precision for R² columns, so model fit and effect sizes can be formatted independently from descriptive values and test statistics.

  • table_continuous_lm() now keeps n as the unweighted analytic sample size in wide and rendered outputs, and can optionally add a separate Weighted n column reporting the sum of case weights.

spicy 0.7.0

CRAN release: 2026-03-30

New features

  • table_continuous() is a new helper for continuous summary tables. It computes descriptive statistics (mean, SD, min, max, confidence interval of the mean, and n) for numeric variables, with tidyselect column selection, optional grouping via by, and multiple output formats (ASCII, tinytable, gt, flextable, Excel, clipboard, and Word).

  • table_continuous() gains effect_size and effect_size_ci arguments. When by is used, effect_size = TRUE adds an “ES” column with the appropriate measure (Hedges’ g, eta-squared, rank-biserial r_rb, or epsilon-squared) chosen automatically based on the test method and number of groups, and effect_size_ci = TRUE appends the confidence interval in brackets.

  • table_continuous() gains a test argument ("welch", "student", or "nonparametric") to choose the group-comparison method, along with independent p_value and statistic display toggles so users can request either or both outputs when by is used.

  • ASCII console tables now split oversized outputs into stacked horizontal panels, repeating the left-most identifier columns so wide freq(), cross_tab(), table_categorical(), and table_continuous() prints stay readable in narrow consoles.

Breaking changes

  • table_categorical() replaces table_apa() as the public helper for categorical summary tables. It uses select and by, supports grouped cross-tabulation or one-way frequency-style tables when by = NULL, and consolidates output formats under a single output argument. Migrate existing table_apa() calls to table_categorical(), use output = "default" for ASCII tables and output = "data.frame" for plain data frames, and replace former output = "wide" / style = "report" paths with the formatted output engines.

  • Excel export now uses openxlsx2 instead of openxlsx for a lighter dependency footprint (no Rcpp compilation required).

Minor improvements

  • Package citation metadata now uses the current package title and CRAN DOI, so citation("spicy") matches DESCRIPTION and points to the package DOI.

  • table_categorical() and table_continuous() now print shorter ASCII titles without appending the input data frame name, and no longer require officer for output = "flextable" alone; officer is now required only for Word export paths that actually write .docx files.

  • table_continuous() now accepts tidyselect syntax in exclude in addition to character vectors, and no longer warns that test is ignored when it is still needed to compute effect sizes.

spicy 0.6.0

CRAN release: 2026-03-23

New features

  • New family of association measure functions for contingency tables: assoc_measures(), contingency_coef(), gamma_gk(), goodman_kruskal_tau(), kendall_tau_b(), kendall_tau_c(), lambda_gk(), phi(), somers_d(), uncertainty_coef(), and yule_q(). Each returns a numeric scalar by default; pass detail = TRUE for a named vector with estimate, confidence interval, and p-value.

  • cross_tab() gains assoc_measure and assoc_ci arguments. When both variables are ordered factors, it automatically selects Kendall’s Tau-b instead of Cramer’s V. The note format changes from Chi-2: 18.0 (df = 4) to Chi-2(4) = 18.0. Numeric attributes (chi2, df, p_value, assoc_measure, assoc_value, assoc_result) are now attached to the output data frame.

  • table_apa() now dynamically labels the association measure column based on the measure used, instead of always showing “Cramer’s V”. New assoc_measure and assoc_ci arguments are passed through to cross_tab().

  • table_apa() gains output = "gt" to produce a gt_tbl object with APA-style formatting, column spanners, and alignment.

  • table_apa() now correctly centers spanner labels over their column pairs in tinytable and flextable output.

  • All association measure functions and assoc_measures() gain a digits argument (default 3) that controls the number of decimal places when printed. The p-value always uses 3 decimal places or < 0.001.

  • detail = TRUE results now print with formatted output (aligned columns, fixed decimal places) via a new print.spicy_assoc_detail() method. assoc_measures() output uses a new print.spicy_assoc_table() method with the same formatting.

  • New bundled dataset sochealth: a simulated social-health survey (n = 1200, 24 variables) with variable labels, ordered factors, survey weights, and missing values. Includes four Likert-scaled life satisfaction items (life_sat_health, life_sat_work, life_sat_relationships, life_sat_standard) for demonstrating mean_n(), sum_n(), and count_n().

Bug fixes

  • count_n() now correctly counts NA values when count = NA and strict = TRUE are both used. List columns are now reported in verbose mode instead of causing silent errors.

  • cross_tab() rescale logic now operates on complete cases only, so the weighted total N matches the unweighted N when missing values are present (consistent with Stata behavior).

  • freq() now uses true NA consistently (instead of the "<NA>" string) in both weighted and unweighted paths. cum_valid_prop is now correctly NA for missing rows. Invalid digits and sort values are rejected with clear error messages.

  • mean_n() and sum_n() now validate min_valid and digits arguments, rejecting non-numeric, negative, or multi-element values.

  • mean_n(), sum_n(), and count_n() no longer trigger a tidyselect deprecation warning when select receives a character vector. Character vectors are now automatically wrapped with all_of().

  • table_apa() now preserves the original factor level order in row variables instead of sorting alphabetically. When drop_na = FALSE, the (Missing) category is placed at the bottom of each variable’s levels. percent_digits, p_digits, and v_digits are now validated.

  • table_apa() p-values no longer wrap across lines in tinytable HTML output.

Breaking changes

  • cramer_v() now accepts a detail argument. By default it returns a numeric scalar (as before). Pass detail = TRUE to get a 4-element named vector (estimate, ci_lower, ci_upper, p_value), or detail = TRUE, conf_level = NULL for a 2-element vector (estimate, p_value) without CI.

spicy 0.5.0

CRAN release: 2026-03-14

New features

  • New table_apa() helper to build APA-ready cross-tab reports with multiple output formats (wide, long, tinytable, flextable, excel, clipboard, word).
  • table_apa() exposes key cross_tab() controls for weighting and inference (weights, rescale, correct, simulate_p, simulate_B) and now handles missing values explicitly when drop_na = FALSE.

Bug fixes

  • count_n() no longer crashes when special = "NaN" is used with non-numeric columns. Passing count = NA now errors with a message directing to special = "NA".
  • cross_tab() fixes a spurious rescale warning for explicit all-ones weights and aligns the Cramer’s V formula with cramer_v().
  • table_apa() no longer leaks global options on error. The simulate_p default is aligned to FALSE.
  • varlist() title generation no longer crashes on unrecognizable expressions.

Minor improvements

  • copy_clipboard() parameter message renamed to show_message.
  • freq() now dispatches printing correctly via S3.
  • Removed unused collapse and stringi from Imports.

spicy 0.4.2

CRAN release: 2026-03-06

  • cross_tab() hardening: improved vector-mode detection (including labelled vectors), stricter weight validation, safer rescaling, and clearer early errors (e.g., explicit y = NULL).
  • cross_tab() statistics are now computed on non-empty margins in grouped tables, avoiding spurious NA results; internal core path refactored to remove dplyr/tibble from computation while preserving user-facing behavior.
  • freq() now errors clearly when x is missing for data.frame input and validates rescaling when weight sums are zero/non-finite.
  • count_n(), mean_n(), and sum_n() regex mode is hardened (regex = TRUE now validates/defaults select safely).
  • mean_n() and sum_n() now return NA (with warning) when no numeric columns are selected.
  • label_from_names() now validates input type (data.frame/tibble required).
  • cramer_v() now returns NA with warning for degenerate tables.
  • Dependency optimization: DT and clipr moved to Suggests; optional runtime checks added in code_book() and copy_clipboard().
  • Tests expanded with regression coverage for all the above edge cases.

spicy 0.4.1

CRAN release: 2025-12-21

  • Fixed CRAN incoming check notes by removing non-standard top-level files.

spicy 0.4.0

  • Print methods have been fully redesigned to produce clean, aligned ASCII tables inspired by Stata’s layout. The new implementation improves formatting, adds optional color support, and provides more consistent handling of totals and column spacing.

  • Output from freq() and cross_tab() now benefits from the enhanced print.spicy() formatting, offering clearer, more readable summary tables.

  • Documentation and internal tests were updated for clarity and consistency.

  • cross_tab() gains an explicit correct argument to control the use of Yates’ continuity correction for Chi-squared tests in 2x2 tables. The default behavior remains unchanged.

  • The documentation of cross_tab() was refined and harmonized, with a clearer high-level description, improved parameter wording, and expanded examples.

  • Minor cosmetic improvements were made to varlist() output: the title prefix now uses vl: instead of VARLIST, and the column name Ndist_val was renamed to N_distinct for improved readability and consistency.

  • Minor cosmetic improvement: ASCII table output no longer includes a closing bottom rule by default.

spicy 0.3.0

CRAN release: 2025-10-22

  • New function code_book(), which generates a comprehensive variable codebook that can be viewed interactively and exported to multiple formats (copy, print, CSV, Excel, PDF).

spicy 0.2.1

CRAN release: 2025-10-04

  • label_from_names() now correctly handles edge cases when the separator appears in the label or is missing.

spicy 0.2.0

CRAN release: 2025-09-25

  • New function label_from_names() to derive and assign variable labels from headers of the form "name<sep>label" (e.g. "name. label"). Especially useful for LimeSurvey CSV exports (Export results -> CSV -> Headings: Question code & question text), where the default separator is ". ".

spicy 0.1.0

CRAN release: 2025-05-05

Initial release

  • Introduces a collection of tools for variable inspection, descriptive summaries, and data exploration.
  • Provides functions to:
    • Extract variable metadata and display compact summaries (varlist()).
    • Compute frequency tables (freq()), cross-tabulations (cross_tab()), and Cramer’s V for categorical associations (cramer_v()).
    • Generate descriptive statistics such as means (mean_n()), sums (sum_n()), and counts (count_n()) with automatic handling of missing data.
    • Copy data (copy_clipboard()) directly to the clipboard for quick export.