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Changelog

Source: NEWS.md

mariposa 0.7.4

This release brings many more results in line with IBM SPSS Statistics 29, makes every analysis result exportable, and improves output and error messages throughout the package. Some results and result objects change; see “Breaking changes”.

Breaking changes

  • Results that now follow SPSS:
    • Weighted describe() reports N as the sum of the weights and a weighted missing count, as SPSS FREQUENCIES and DESCRIPTIVES do; Kish’s effective N remains available in $results.
    • Weighted crosstab() rounds each weighted cell count before margins and percentages are computed (SPSS /COUNT ROUND CELL).
    • binomial_test() uses the category of the first valid case as Group 1 and, for a test proportion other than 0.5, reports the one-tailed exact p-value (new column alternative).
    • One-sample t_test(): mean_diff and its confidence interval refer to the difference between the mean and mu.
    • tukey_test() and scheffe_test() report one row per pair in the “(I) - (J)” orientation, with the new columns SE and t_value; weighted analyses use the error mean square and degrees of freedom of the weighted ANOVA.
    • wilcoxon_test() and pairwise_wilcoxon() compute Z from the smaller rank sum, so Z is never positive; the new column z_based_on gives the direction. The weighted two-sided mann_whitney() follows the same convention.
    • chi_square() and phi(): Phi of a 2x2 table carries the sign of the association; with correct = TRUE, Phi and Cramér’s V are based on the Pearson chi-square; Goodman’s gamma is reported for ordinal variables only.
    • efa() uses the SPSS algorithms for varimax, oblimin and promax rotation and reflects factors to positive loading sums.
    • ancova() parameter estimates use indicator coding with the last category as reference.
    • rec(rules = "rev") reverses on the scale range defined by the value labels instead of the observed range.
  • Result objects:
    • The w_*() functions return one long table (Variable, the statistic, n or weighted_n, effective_n, missing).
    • Grouped levene_test() results contain the grouping variables as columns; the grouped method takes its variables through ....
    • chi_square() no longer returns label_maps (the tables carry the labels); dunn_test() and pairwise_wilcoxon() also return their comparisons as $results.
    • std(), center(), pomps() and to_numeric() return NA for SPSS missing values; std() and center() no longer add the weights column to the result.
    • codebook() returns its result visibly.
  • Input handling:
    • Argument names must be written out in full. A misspelled or abbreviated name such as weight = or percent = is an error that names the intended argument.
    • Grouping variables of group_by() data are excluded from variable selections, with a message.
    • Weights without any positive value are an error.
    • The rank tests reject unordered factors and rank ordered factors by their level order.
    • to_label() on a data frame converts only fully labelled variables.
    • frequency() omits empty factor levels unless show_unused = TRUE.
    • row_count(count = c(4, 5)) counts cells equal to any of the values.
  • reliability() removes items without variance (with a warning) and reports omega as NA for a Heywood solution.

New features

  • Every analysis result can be exported: as.data.frame() and tibble::as_tibble() work for all result classes, broom::tidy() for all tests and descriptive results, and write_xlsx() writes analysis results (cross-tabulations in the SPSS layout) as well as lists that combine results and data frames.
  • weights accepts a column name (unquoted or as a string), all_of(), an expression such as sampling_weight * 2, or a numeric vector, in every function.
  • codebook() embeds the HTML codebook in R Markdown and Quarto documents.
  • factorial_anova() and ancova() support group_by(); ancova() reports the marginal means of the main effects and has a levene_test() method.
  • describe() gains show = "min" and show = "max".
  • fisher_test() reports the odds ratio with its 95% confidence interval for 2x2 tables and offers a Monte Carlo p-value for large tables (arguments simulate.p.value and B).
  • efa() reports the extraction sums of squared loadings, the sample size, descriptive statistics and the number of rotation iterations; oblimin rotation no longer requires the GPArotation package.
  • logistic_regression() accepts any outcome with two values (for example 1/2 codes, labelled, character or logical) and shows the dependent variable encoding.
  • confint(), profile(), update() and step() work on regression results; weighted linear_regression() methods use the same degrees of freedom as summary().
  • rec() accepts value lists ("1,2=1") and an explicit scale range ("rev(1, 5)").
  • var_label(x) <- "label" and var_label(data) <- list(...) set variable labels.
  • find_var(fixed = TRUE) searches for literal text; na_frequencies(), strip_tags() and untag_na() accept data frames.
  • pairwise_wilcoxon() reports the number of cases of each pair.
  • chisq_gof(expected = ) applies to every selected variable, matches named proportions by category and accepts counts, as SPSS /EXPECTED does.

SPSS compatibility

  • The following results now match SPSS Statistics 29: the p-value of Goodman’s gamma; Levene’s test of ancova(); standard errors, confidence intervals and significance of weighted tukey_test() and scheffe_test(); weighted cell standard deviations of factorial_anova() and ancova(); the expected counts and residuals of chisq_gof(); all rotations and the ML extraction of efa().
  • Tests with labelled or numeric grouping variables order the groups by code and show value labels, as SPSS does.
  • Printed values are rounded half up, as in SPSS.
  • The validation suite compares about 14,600 values from SPSS reference output; see vignette("spss-compatibility").

Improvements

  • Output follows the SPSS table layouts: Group Statistics and Independent Samples Test (t_test()), Descriptives and Robust Tests of Equality of Means (oneway_anova()), valid and missing totals (frequency()), Item-Total Statistics (reliability()), Total Variance Explained (efa()).
  • Group headers, test groups and categories show value labels; titles show variable labels; labels are never truncated.
  • Consistent number formatting: fixed decimals per column, p-values as <.001, whole-number counts without thousands separators, aligned columns for labels with non-ASCII characters.
  • Tables are printed as one block wherever they can be displayed; in the console, a table is split into column blocks only when it is wider than the console.
  • frequency(), crosstab() and multiple_response() print their full tables.
  • Results that cannot be computed print “not computed” with the reason, and warnings name the variable and group.
  • Grouped analyses skip a group that cannot be computed, with a warning naming it, instead of stopping.
  • reliability() explains a negative Cronbach’s alpha and names the items to check; efa() reports a singular correlation matrix like SPSS.
  • Calling a function without a required argument, or on a data frame without rows, gives a clear error message.
  • kendall_tau(), goodman_gamma(), codebook(), untag_na(), write_spss() and write_xlsx() are considerably faster on large data.
  • Exact tests (fisher_test(), mcnemar_test(), binomial_test()) and logistic_regression() work with very large (expansion) weights.
  • citation("mariposa") gives the DOI of the CRAN package.
  • Compatible with the upcoming haven release, which stores the tags of tagged missing values in lower case: SAS special missing values are still shown as .A, .B, …, and write_xpt() shortens over-long variable names itself (@hadley, #22).

Bug fixes

  • Weights imported with read_spss(), and labelled data used without loading the haven package, work in all functions.
  • marginal_effects() computes correct effects for transformed predictors such as I(x^2) or log(x).
  • read_sas() works with its default arguments.
  • read_spss() and write_spss() preserve SPSS missing-value definitions; write_spss() and write_stata() keep value labels created by rec() and the original codes of to_label() factors.
  • The import functions detect files of another format and name the right reader.
  • to_label() and to_numeric() keep the original codes and keep codes that share a label text apart.
  • rec() keeps SPSS missing-value types and warns about values that match no rule.
  • write_xpt() warns about shortened variable names; write_xlsx() creates unique sheet names.
  • %>% is available after library(mariposa).
  • set_na(), read_spss() and read_xlsx() keep more than 26 missing-value codes of a variable apart with the upcoming haven release, which no longer distinguishes upper- and lower-case tags.

mariposa 0.7.3

CRAN release: 2026-09-28

Maintenance release: internal changes for CRAN policy compliance; no user-visible changes.

mariposa 0.7.2

Maintenance release: internal changes for CRAN policy compliance, plus the user-visible changes below.

Improvements

  • The examples of the import and export functions now run as complete write-and-read roundtrips through temporary files.
  • ancova(), factorial_anova() and the printing of correlation matrices restore changed options() settings reliably, even when interrupted.
  • The package description cites the method references: IBM SPSS Statistics Algorithms, Dallal and Wilkinson (1986) for the Lilliefors correction, and Haberman (1973) for adjusted standardized residuals.

Bug fixes

  • unlabel() no longer fails on data frames with integer columns, such as the Likert items in survey_data.
  • write_xpt() no longer fails on integer columns.

mariposa 0.7.1

Maintenance release: internal changes for CRAN policy compliance, plus two small robustness improvements.

Improvements

  • mariposa declares minimum versions of its dependencies (cli >= 3.0.0, dplyr >= 1.0.0, rlang >= 1.0.0, tidyselect >= 1.1.0, tibble >= 3.0.0, htmltools >= 0.5.0), so outdated installations are updated automatically instead of failing at run time.
  • set_na() applied to a whole data frame reports clearly that the haven package is required when it is not installed.

mariposa 0.7.0

Adds normality tests, partial correlations, multiple-response analysis and average marginal effects.

New features

  • New normality_test() produces the SPSS EXAMINE “Tests of Normality”: Kolmogorov-Smirnov with Lilliefors significance correction and Shapiro-Wilk (for 3 to 5,000 cases), computed as in SPSS. It supports tidyselect and group_by(); it has no weights argument because neither test has a well-defined form for fractional frequency weights.
  • New partial_cor() computes partial correlations controlling for one or more variables (SPSS PARTIAL CORR, Stata pcorr), with zero-order correlations for comparison, two-tailed tests, listwise deletion, weights, group_by() support and a correlation matrix for three or more variables.
  • New marginal_effects() computes average marginal effects for logistic_regression() models (Stata margins, dydx(*)): average derivatives on the probability scale for continuous predictors, average discrete changes relative to the reference level for factors, and delta-method standard errors, with weights and group_by() support. For linear_regression() models it explains that B already is the marginal effect.
  • New multiple_response() analyzes “check all that apply” questions (SPSS MULT RESPONSE dichotomy sets): frequencies as percent of responses and percent of cases, and with by = a crosstab with case-based column percentages. It supports weights and group_by().
  • All four functions provide a compact print() and a detailed summary().
  • normality_test(), partial_cor() and multiple_response() are not yet compared with SPSS output; SPSS has no procedure for average marginal effects.

Bug fixes

  • Formulas on the reference pages of the package website render correctly again.

mariposa 0.6.17

New features

  • crosstab() computes expected cell counts and adjusted standardized residuals (SPSS CROSSTABS /CELLS=EXPECTED ASRESID). summary(result, residuals = TRUE) shows them below each cell, with a note on the |adjusted residual| > 2 rule of thumb; the matrices are available as $expected and $adj_residuals.
  • Weighted tables use the unrounded weighted cell counts for these statistics.
  • The adjusted residuals agree with those of chisq.test(); they are not yet compared with SPSS output.

mariposa 0.6.16

Maintenance release: internal changes for CRAN policy compliance; statistical results are unchanged.

Improvements

  • ?mariposa opens the package help page.
  • The help pages of the predict() and anova() methods of both regression functions, and of the print() method for w_quantile() results, describe their return values.
  • The set_na() example runs, and the efa() oblimin example runs only when the optional GPArotation package is installed.

mariposa 0.6.15

Correctness improvements for the regression functions.

New features

  • The coefficients table of linear_regression() shows confidence intervals for B (SPSS /STATISTICS CI) at the level set by conf.level; summary(..., conf_int = FALSE) hides them.

Improvements

  • The documentation of factors = "dummy" explains that ordered factors receive polynomial contrasts rather than dummy coding, and how to obtain dummy coding.
  • The SPSS compatibility vignette states that logistic_regression() has not yet been compared with SPSS output.

Bug fixes

  • Weighted logistic_regression() computes the -2 log-likelihood, the omnibus chi-square and the Cox & Snell and Nagelkerke R-squared correctly with fractional weights; unweighted and integer-weighted results are unchanged.
  • linear_regression(use = "pairwise") no longer silently drops interactions and transformed terms; such formulas raise an error that suggests use = "listwise".
  • linear_regression() no longer fails on perfectly collinear predictors: these are excluded from the coefficient table with a message, as in SPSS, and the ANOVA degrees of freedom count only estimated terms.
  • The classification table of logistic_regression() shows the cutoff actually used instead of always 0.50.

mariposa 0.6.14

Robustness and display improvements for codebook().

New features

  • New view argument of codebook() (default interactive()) controls whether the HTML codebook opens in the RStudio Viewer; writing with file = is unaffected. print() mentions the Viewer only when it was opened.

Improvements

  • Numeric values and ranges are formatted consistently in console, HTML and Excel output: fractional values with 4 significant digits, whole numbers as integers, and no scientific notation.
  • Percentages and effective n in the frequency sheets of write_xlsx(cb, frequencies = TRUE) are rounded to 2 decimals.
  • Range displays include the number of distinct values (e.g. “18 - 95 (78 distinct)”).
  • Factor levels respect max_values and are truncated with an “… (N more)” note; long character values are truncated to max_len.
  • Invalid max_values or max_len values, and a file = path in a nonexistent directory, raise a clear error before any work is done.
  • Output uses singular forms where appropriate (“1 variable”) and shows “(no observations)” for data frames without rows.

Bug fixes

  • Variables with value labels but no variable label no longer show a spurious variable label and are no longer counted as labelled.
  • codebook() no longer fails on inline data expressions such as codebook(data.frame(...)).
  • List columns are skipped with a warning instead of causing an error.
  • Missing-value codes, labels and counts appear consistently: in the HTML codebook also for variables displayed as a range, and in summary() in a new “Missing values” section, so show_na now takes effect on the console.

mariposa 0.6.13

Adds McDonald’s omega to reliability().

New features

  • reliability() reports McDonald’s omega, estimated from a one-factor maximum-likelihood model, also for weighted data: omega (raw, analogous to raw alpha) and omega_std (standardized, analogous to standardized alpha).
  • The item-total table gains an omega_if_deleted column (NA when fewer than 3 items remain).
  • print() shows omega next to alpha; summary() adds omega to the Reliability Statistics block and the Item-Total table.
  • For scales with fewer than 3 items (with a warning), or when the factor model does not converge, the omega fields are NA; alpha is unaffected.
  • Omega has not yet been compared with SPSS output; it is cross-checked against psych::omega() and a one-factor confirmatory factor analysis. The help page notes this.

mariposa 0.6.12

Corrections to weighted rank statistics and clearer documentation of which results are compared with SPSS.

Improvements

Bug fixes

  • Weighted kendall_tau() computes the tau-b denominator correctly for data with pairs tied on both variables.
  • Weighted kruskal_wallis() uses the correct grand mean rank, so H is no longer too large.

mariposa 0.6.11

Removes the argument names and result columns deprecated in 0.6.9 and 0.6.10.

Breaking changes

  • The dot-case argument names of codebook() (show.id, show.type, show.labels, show.values, show.freq, show.na, show.unused, max.values, max.len, sort.by.name) and of val_labels() and drop_labels() (drop.na) raise an error; use the snake_case names (e.g. show_id, drop_na).
  • The duplicate result columns are removed; use the canonical names: chi_squared instead of chi_sq (chisq_gof(), friedman_test()) and instead of statistic (mcnemar_test()), r_effect instead of effect_size_r (mann_whitney()), and F_statistic instead of F_stat (oneway_anova()). Statistical values are unchanged.

mariposa 0.6.10

Harmonizes result column names across functions.

Breaking changes

Improvements

mariposa 0.6.9

Completes the switch to snake_case argument names.

Breaking changes

  • The dot-case argument names deprecated in 0.6.8 raise an error in frequency(), fre(), rec(), to_label(), to_character(), to_numeric(), read_spss(), read_por(), read_stata(), read_sas() and read_xpt(); use the snake_case names (e.g. sort_frq instead of sort.frq).
  • In functions that select variables through ..., an old argument name raises an informative error instead of being taken as a variable selection.
  • The arguments of codebook() are renamed to snake_case (show_id, show_type, show_labels, show_values, show_freq, show_na, show_unused, max_values, max_len, sort_by_name), as is drop_na in val_labels() and drop_labels(). The old names still work in this release with a warning (once per session).
  • The display options stored in codebook() results (result$options) use snake_case keys.

mariposa 0.6.8

Argument names switch to snake_case.

Breaking changes

  • Arguments with dots in their names are renamed to snake_case; the old names still work in this release with a warning (once per session). The base R conventions na.rm, conf.level and var.equal are unchanged. Renamed arguments:
  • t_test() results no longer contain the duplicate columns CI_lower and CI_upper; use conf_int_lower and conf_int_upper.

Improvements

  • frequency() validates sort_frq ("none", "asc", "desc") and show_labels (TRUE, FALSE, "auto") with a clear error; a misspelled sort_frq previously produced an unsorted table silently.
  • The vignettes use the new argument names.

mariposa 0.6.7

Uniform output for all analysis functions; statistical results are unchanged.

Improvements

mariposa 0.6.6

Maintenance release: internal restructuring; statistical results are unchanged.

Improvements

  • tukey_test() and scheffe_test() print aligned tables and show p-values in SPSS style.
  • Input validation errors name the function called by the user rather than an internal helper.
  • Internal functions no longer have help pages.

mariposa 0.6.5

Maintenance release; statistical results are unchanged.

Improvements

  • tidyr is no longer a dependency.
  • Errors inside grouped analyses keep the original error message.
  • The error for a missing haven installation names the function that was called.
  • The function reference on the package website lists phi(), cramers_v() and goodman_gamma().

mariposa 0.6.4

Improves the accuracy of several statistics and makes functions behave more consistently. Some results change slightly, in each case toward the standard reference implementations.

Breaking changes

  • kruskal_wallis() reports its effect size as epsilon_squared (previously eta_squared).
  • phi(), cramers_v() and goodman_gamma() return the effect size as a single number; use chi_square() for the full test output.
  • Negative weights are rejected with a clear error in all functions.
  • oneway_anova() always reports both the classical and the Welch results (as SPSS ONEWAY does), so its var.equal argument is deprecated; ss_type in factorial_anova() and ancova() is deprecated in favor of the SPSS-standard Type III sums of squares.

New features

  • linear_regression() reports collinearity diagnostics (Tolerance and VIF per model term); summary(..., collinearity = FALSE) hides them.

Improvements

  • The weighted Wilcoxon signed-rank test (wilcoxon_test(), pairwise_wilcoxon()) uses frequency-expansion mid-ranks: integer weights give exactly the expanded-data result, and weights of 1 reproduce the unweighted test. Mean ranks in weighted kruskal_wallis() and dunn_test() follow the same convention.
  • The weighted median equals the weighted 50th percentile, and unweighted quantiles follow the SPSS definition (HAVERAGE, type 6) throughout.
  • frequency() header statistics use the same formulas as describe() and the w_* functions.
  • write_spss() protects valid values when many missing-value codes must be combined into a range, and explains what it does.
  • Post-hoc tests report when a computation cannot be carried out instead of skipping it silently.

Bug fixes

  • The significance test of Kendall’s tau-b agrees with stats::cor.test() and the SPSS formula, notably for heavily tied data such as binary variables.
  • The asymptotic p-value of mann_whitney() matches its reported Z (SPSS convention, no continuity correction).
  • Degrees of freedom in weighted linear_regression() and in the omnibus test of logistic_regression() are derived from the fitted model terms, improving results for models with dummy-coded factors or interactions.
  • The weighted two-sample t_test() respects var.equal for its primary result.
  • Correlation functions return NA for constant variables instead of an error.
  • frequency(show.unused = TRUE) works on variables tagged with set_na() or read_spss(), and sort.frq sorts by frequency with a monotone cumulative percentage.
  • Significance stars use one boundary convention everywhere, matching the printed legend.
  • logistic_regression() shows separation and convergence warnings again.
  • Grouped single-variable w_* results print their statistics again.

mariposa 0.6.3.2

Bug fixes

  • rec() matches single decimal values (e.g. "3.6=2") reliably, even when the stored value carries floating-point representation error.

mariposa 0.6.3.1

Bug fixes

mariposa 0.6.3

Regression results are now standard R model objects.

Breaking changes

  • linear_regression() and logistic_regression() results are the fitted lm or glm objects themselves (classes c("linear_regression", "lm") and c("logistic_regression", "glm", "lm")), with the mariposa tables attached. The $model element is gone; use the result directly (e.g. predict(r, ...)).
  • The $coefficients table is renamed to $coef_table (coef(r) returns the coefficient vector), and the $anova table to $anova_table (anova(r) returns R’s sequential ANOVA table).

New features

mariposa 0.6.2

Breaking changes

  • linear_regression() and logistic_regression() gain a factors argument. The new default factors = "dummy" expands a factor with L levels into L - 1 dummy variables, as lm() and glm() do; previously, factor levels were silently converted to integer codes. factors = "numeric" restores this SPSS-style treatment and lists the converted variables in a message.
  • With use = "pairwise", factor predictors require factors = "numeric" or use = "listwise"; otherwise an informative error is raised.

Bug fixes

  • Weighted linear_regression() and logistic_regression() use the unrounded sum of weights in degrees of freedom, F, R-squared and pseudo R-squared, bringing results closer to SPSS; the displayed N stays rounded.
  • summary(descriptives = TRUE) for linear_regression() prints the Descriptive Statistics table.
  • Printing weighted linear_regression() results with non-integer degrees of freedom no longer fails.
  • The examples of the regression summary() methods use the correct argument names.

mariposa 0.6.1

Brings weighted results closer to SPSS (weighted results may shift slightly) and documents the validation status of each function.

Improvements

  • New vignette("spss-compatibility") shows the validation status of each function.
  • The spearman_rho() documentation states that weights are used only to select cases, not in the rank correlation itself.
  • The pearson_cor() documentation explains that raw expansion weights produce too narrow confidence intervals and should be normalized first.
  • PMCMRplus and survey are no longer suggested dependencies.

Bug fixes

  • Weighted t_test() uses the unrounded sum of weights for variances, standard errors and degrees of freedom, including the Welch correction.
  • Weighted oneway_anova() computes variances, standard errors, confidence intervals and within-groups degrees of freedom as SPSS ONEWAY does.
  • Weighted levene_test() uses the unrounded sum of weights in its degrees of freedom.
  • dunn_test() applies the tie correction to its standard errors, so |Z| is no longer underestimated for tied data such as Likert scales.
  • Weighted friedman_test() applies the tie correction like the unweighted test.
  • Weighted skewness and kurtosis in describe() use the bias-corrected formulas of w_skew(), w_kurtosis() and SPSS FREQUENCIES.
  • Weighted and unweighted quantiles follow SPSS FREQUENCIES (HAVERAGE, type 6) instead of R’s default type 7.

mariposa 0.6.0

Adds functions for label management, data transformation, row operations and variable search.

Breaking changes

  • scale_index() is removed; use row_means(), which provides the same functionality.

New features

  • New var_label() gets and sets variable labels.
  • New val_labels() gets and sets value labels; .add = TRUE extends existing labels.
  • New copy_labels() copies all label attributes to matching columns of another data frame, e.g. to restore labels lost in dplyr operations.
  • New drop_labels() removes value labels for values that do not occur in the data.
  • New to_label() and to_character() convert labelled vectors to factors (levels in code order) or character; new to_numeric() and to_labelled() convert factors and other vectors to numeric or labelled vectors.
  • New set_na() declares values as missing, by default as tagged NAs that na_frequencies(), frequency() and codebook() recognize; named arguments set codes per variable.
  • New unlabel() removes all label metadata and returns plain R vectors.
  • New rec() recodes values with a compact string syntax (e.g. "1:2=1 [Low]; 3:5=2 [High]"), including ranges, min/max, copy and value labels.
  • New to_dummy() creates 0/1 indicator variables, optionally named after the value labels.
  • New std() standardizes variables with four methods ("sd", "2sd", "mad", "gmd"), with weights, group_by() and a robust option.
  • New center() centers variables at the grand mean or group mean, with weights.
  • New row_means() and row_sums() compute row-wise means and sums with a minimum number of valid values (min_valid, like SPSS MEAN.x()); new row_count() counts specific values per row.
  • New find_var() searches variables by name or label.

mariposa 0.5.6

Adds export to SPSS, Stata and SAS transport files.

New features

  • New write_spss() writes SPSS .sav files and converts tagged NAs back to user-defined missing values, for lossless roundtrips with read_spss(); it supports byte, none and zsav compression.
  • New write_stata() writes Stata .dta files (versions 8 to 15), with tagged NAs as extended missing values (.a to .z).
  • New write_xpt() writes SAS transport .xpt files (versions 5 and 8), with tagged NAs as special missing values (.A to .Z, ._).
  • Data imported from one format can be exported to another (e.g. SPSS to Stata), with missing-value types converted automatically.

mariposa 0.5.5

Adds Excel import and export with label preservation.

New features

  • New read_xlsx() reads Excel files and restores variable labels, value labels and tagged NAs from files written by write_xlsx(); other Excel files are read as plain data.
  • New write_xlsx() exports data frames (with a “Labels” sheet for variable labels, value labels and missing-value codes), codebooks (with optional frequency sheets, frequencies = TRUE) and named lists (one sheet per element) to Excel. Missing-value codes stay visible in the data sheet.
  • Excel import and export require the suggested package openxlsx2.

mariposa 0.5.4

Adds import of Stata, SAS and SPSS portable files.

Breaking changes

  • The spss_code column of na_frequencies() is renamed to code and contains character values, such as "-9" for SPSS or ".a" for Stata.

New features

  • New read_stata() reads Stata .dta files and integrates extended missing values (.a to .z) with na_frequencies(), frequency() and codebook().
  • New read_sas() reads SAS .sas7bdat files, optionally with a catalog file (.sas7bcat) for value labels, and integrates special missing values (.A to .Z, ._).
  • New read_xpt() reads SAS transport .xpt files with support for special missing values.
  • New read_por() reads SPSS portable .por files with the same tagged NA support as read_spss().

Improvements

mariposa 0.5.3

Adds SPSS import with tagged missing values.

New features

  • New read_spss() reads SPSS .sav files and keeps user-defined missing values as tagged NAs, so different types of missing data (e.g. “no answer”, “not applicable”) remain distinguishable while behaving as NA in R. It also handles large survey files.
  • New na_frequencies() shows the types of missing values in a variable with counts, original codes and labels.
  • New untag_na() converts tagged NAs back to their original SPSS missing-value codes.
  • New strip_tags() converts tagged NAs to regular NA.

Improvements

  • frequency() lists each type of tagged NA in its own row with code and label, followed by “Total Valid” and “Total Missing” rows.

Bug fixes

  • frequency(show.unused = TRUE) no longer lists labels of tagged NAs as unused categories.

mariposa 0.5.2.2

New features

mariposa 0.5.2

New features

  • New codebook() creates an interactive HTML data dictionary in the RStudio Viewer, showing each variable’s position, name, type, label, values, value labels and frequencies. It is built with htmltools, a new dependency.
  • Printing a codebook gives a short overview; summary() gives a text version with selectable sections (overview, variable_details, value_labels).
  • codebook() supports tidyselect, survey weights for weighted frequencies, and sorting by variable name.

mariposa 0.5.1

New features

Improvements

  • All print and summary methods are documented, and the examples of the analysis functions demonstrate both output levels.

Bug fixes

  • The examples of ancova() and factorial_anova() use the correct dv/between interface, and examples refer to the education variable correctly.

mariposa 0.5.0

Adds factorial ANOVA and ANCOVA.

New features

  • New factorial_anova() performs between-subjects ANOVA with up to three factors and Type III sums of squares, as SPSS UNIANOVA does: main effects, all interactions, partial eta squared, R-squared and Levene’s test. It works with tukey_test(), scheffe_test() and levene_test().
  • New ancova() performs analysis of covariance (SPSS UNIANOVA with WITH): ANOVA table, parameter estimates, estimated marginal means adjusted for the covariates, and Levene’s test; it supports up to three factors and multiple covariates.
  • Both functions support survey weights through weighted least squares, as SPSS /REGWGT does (including Levene’s test), handle unbalanced designs correctly, and do not require the car package.

mariposa 0.4.0

Adds exact and goodness-of-fit tests and non-parametric post-hoc tests.

New features

  • New fisher_test() performs Fisher’s exact test of independence, recommended for small samples or expected cell counts below 5; it supports weights, multiple variables and group_by().
  • New chisq_gof() tests whether a categorical variable follows an expected distribution (equal proportions by default, or custom proportions), with residuals, weights and multiple variables.
  • New mcnemar_test() tests changes in paired proportions of two dichotomous measurements (e.g. before/after), with asymptotic and exact p-values, the 2x2 table, continuity correction and weights.
  • New dunn_test() performs Dunn’s pairwise post-hoc comparisons on kruskal_wallis() results, with adjustable p-value correction (Bonferroni, Holm, BH and others).
  • New pairwise_wilcoxon() performs pairwise Wilcoxon signed-rank post-hoc comparisons on friedman_test() results, with adjustable p-value correction.

mariposa 0.3.1

New features

  • efa() supports maximum likelihood extraction (extraction = "ml") with a goodness-of-fit chi-square test, initial communalities (squared multiple correlations) and uniquenesses; survey weights are supported.
  • efa() supports Promax rotation (rotation = "promax"), an oblique rotation that reports the pattern matrix, structure matrix and factor correlation matrix.

mariposa 0.3.0

Adds non-parametric tests.

New features

  • New kruskal_wallis() compares three or more independent groups on ordinal data (non-parametric alternative to one-way ANOVA), with an effect size, weights, group_by() and multiple variables.
  • New wilcoxon_test() compares two paired measurements (Wilcoxon signed-rank test), with rank categories (negative, positive, ties) and effect size r.
  • New friedman_test() compares three or more related measurements (non-parametric alternative to repeated-measures ANOVA), with Kendall’s W as effect size.
  • New binomial_test() tests whether an observed proportion matches an expected value (exact binomial test), for one or more binary variables.

mariposa 0.2.0

Adds scale analysis and regression.

New features

  • New reliability() computes Cronbach’s alpha with item statistics: corrected item-total correlations, alpha if item deleted, and the inter-item correlation matrix.
  • New efa() performs exploratory factor analysis with principal component extraction and Varimax or Oblimin rotation (Oblimin requires the suggested package GPArotation), including KMO, Bartlett’s test, communalities and a sorted loading matrix with a configurable blank threshold.
  • New scale_index() computes mean indices across items, with min_valid like SPSS MEAN.x().
  • New pomps() rescales values to percent of maximum possible scores (0-100).
  • New linear_regression() fits linear models with SPSS-style output (coefficients with B, SE, Beta, t and p; ANOVA table; R, R-squared and adjusted R-squared), using a formula or the SPSS-style dependent/predictors interface.
  • New logistic_regression() fits logistic models with odds ratios, Wald statistics, pseudo R-squared (Nagelkerke, Cox & Snell, McFadden) and a classification table.
  • All new functions support survey weights and group_by().

mariposa 0.1.0

Breaking changes

  • gamma() is renamed to goodman_gamma() so that it no longer masks base::gamma().
  • Result classes no longer carry the _results suffix and match the function name (e.g. t_test instead of t_test_results).

Improvements

  • Result objects store only the columns needed for post-hoc tests, which reduces memory use.
  • t_test() checks that conf.level is between 0 and 1; t_test() and oneway_anova() check that the selected variables are numeric; chi_square() warns when expected cell counts are below 5.
  • Error and warning messages and printed output are formatted with the cli package.
  • Help pages link related functions by family (descriptive statistics, hypothesis tests, correlation, post-hoc tests, weighted statistics).

Bug fixes

  • Weighted variance and standard deviation use the SPSS frequency-weight formula consistently.
  • The asymptotic standard error of Goodman and Kruskal’s gamma uses the ASE0 formula (Agresti, 2002).
  • Weighted Cohen’s d in t_test() uses weighted means and the pooled weighted standard deviation.
  • Weighted kurtosis uses the SPSS sample-corrected (Type 2) formula.