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Changelog

Source: NEWS.md

EFAtools 1.0.0.9000

Comparing and Averaging Solutions

  • efa_compare() display settings (as set via print(), format(), or plot()) are now saved and reused, so you can change them after the fact without rerunning the comparison.

  • efa_compare()’s are_equal is now NA when the compared integer parts differ; 0 is reserved for values whose integer parts agree but decimals do not.

  • Fixed efa_average()’s progress bar, which could previously trigger spurious warnings.

  • efa_group() now declares se as a formal argument; a supplied value is dropped with a warning pointing to b_boot (previously it was silently matched to seed and raised a confusing error).

  • efa_compare()’s elementwise differences are now signed (previously unsigned).

  • efa_compare(reorder = "congruence") now also aligns the sign of single-factor solutions, so two one-factor solutions differing only in sign are no longer reported as maximally different.

  • Fixed efa_compare()’s handling of missing loadings, which previously inflated diff_corres and diff_corres_cross; both are now NA when no row can be compared.

  • efa_compare() now rejects invalid input: empty pairs, infinite values, non-numeric content, or a negative thresh.

  • efa_group() now rejects b_boot = 1.

Data Screening and Simulation

  • Fixed efa_screen()’s multivariate-outlier check, which could previously discard the best solution when a robust subset’s covariance matrix was singular.

  • Fixed efa_screen()’s robust covariance estimation, which previously fell back to classical Mahalanobis distances when variable scales differed greatly.

  • efa_screen()’s multicollinearity verdict is now based on the condition index alone, and its bands follow Belsley (1991): an index of 10 or less is rarely of interest, an index above 30 flags a near linear dependency, and its relative strength above 30 is graded by its position on the scale 1, 3, 10, 30, 100, 300, 1000 as moderate, strong, or very strong. The determinant is reported as a plain number, since a small determinant no longer by itself flags multicollinearity.

  • efa_screen() now withholds the Henze-Zirkler p-value (previously an uninformative exact 0 or 1) when its null distribution has no resolvable spread; the hz result then carries class efa_screen_no_hz.

  • Mardia’s skewness and kurtosis in efa_screen() now always apply small-sample/exact-moment corrections (previously only below 20 observations, or asymptotic); both statistics, their p-values, and the normality verdict can change. The mardia$small_sample flag is dropped.

  • efa_screen() now requires at least 3 variables (error efa_screen_too_few_vars).

  • efa_screen()’s outlier_cutoff must now lie between 0.5 and 0.9999 (default 0.975 unchanged).

  • efa_simulate() gains missing_vars to restrict missingness to selected variables.

  • With marginals = "VM" and force_pd = TRUE, efa_simulate()’s returned population now reflects the projected correlations actually used to generate the data.

  • efa_simulate() now gives a clear error when a requested empirical-marginal category is too rare to reproduce at the sample size.

  • efa_simulate() and efa_power(mode = "simulation") now reject an R whose diagonal is not 1.

Factor Retention

  • The type argument of efa_ekc() and ekc_type of efa_retain() are deprecated and ignored: the empirical Kaiser criterion is now always computed as in Braeken and van Assen (2017), so suggested factor counts can differ from earlier results, and efa_retain() no longer reports EKC_AM2019.

  • efa_cd() now gives a clear error when data contain constant variables.

  • efa_smt()’s RMSEA rule now stops when the lower confidence bound cannot be computed, instead of continuing to later models.

  • efa_nest() results can now be plotted.

  • Factor-retention functions now reject the unsupported arguments seed, se, b_boot, and ci (use set.seed() for randomness).

  • efa_parallel() now shares simulated datasets between "PCA" and "SMC" references when both are requested, halving simulation time; results for a given seed can differ from before.

  • A failed simulation block in efa_parallel() is now redrawn on its own instead of restarting the whole batch.

  • efa_ekc(), efa_nest(), and efa_smt() now require a sample size larger than the number of variables (error efa_n_too_small, as efa_parallel() already required); in efa_retain() the affected criterion is listed under not_run instead of stopping the call.

  • efa_map() now stops the grid and warns (efa_map_truncated, recording the reached point in m_last) when a residual variance hits zero, instead of continuing with stale, uncomputed values.

  • efa_cd() now gives the error efa_cd_degenerate_population when N_pop is smaller than 2.

  • Count arguments of the retention criteria (n_factors_max, N_samples, max_iter of efa_cd(); n_factors, N, n_vars, n_datasets of efa_parallel(); n_datasets of efa_hull(); N, n_datasets of efa_nest()) must now be at least 1.

  • Factor counts in retention output no longer print in scientific notation (e.g. 3e+00) under a negative options(scipen).

Input Validation and Correlation Handling

  • efa_fit() and efa_average() now reject N = 0 (N = NA still means an unknown sample size).

  • efa_* arguments with fixed choices are now case-insensitive and accept unambiguous abbreviations.

  • Correlation matrices supplied as data frames are now recognised and analysed correctly.

  • Passing NULL to a choice-valued argument now uses its documented default.

  • Correlation-matrix smoothing now consistently returns a positive-definite matrix, including for nearly singular matrices.

  • A square, non-symmetric matrix is now refused as a correlation matrix (error efa_input_not_symmetric) instead of being treated as raw data.

  • A correlation matrix is now considered singular when its smallest-to-largest eigenvalue ratio falls below n_vars * machine epsilon, rather than only when solve() fails; some previously accepted, ill-conditioned matrices now give the error efa_cor_singular.

  • efa_fit() now gives the error efa_n_factors_object when a factor-retention object is supplied as n_factors.

Missing Data and Multiple Imputation

  • efa_mi()’s rmsr_upper argument is deprecated and ignored (it never affected the reported RMSR); EFA_POOLED() still accepts it silently.

  • efa_mi() now rejects mids objects from mice; convert them first with mice::complete(x, "all").

  • With target_method = "consensus", efa_mi() now starts the search for the common rotational target at the imputation closest to all others rather than the first; pooled results no longer depend on the order of data_list (they can differ from earlier versions). Pass consensus_args = list(start = 1) to recover the previous behaviour.

  • efa_mi()’s pooled RMSEA is now capped at 1, and its confidence bounds are withheld when they do not contain the point estimate.

  • efa_mi()’s pooled communalities are now named communalities (MI$h2 is gone; use MI$communalities, SE$communalities, CI$communalities).

  • efa_mi()’s pooled fit_indices now hold the same elements in the same order across every standard-error route; the sandwich route gains pool_method (as NA).

  • An efa_mi() solution now carries an mi_admissibility component, reporting the Heywood flags of the individual imputations.

  • A cor_method = "fiml" fit with analytic standard errors now withholds loading standard errors (warning efa_se_unreliable) when the rotational orientation is weakly determined, matching the other standard-error routes.

Model Estimation

  • The "uls" (minimum residual) estimator’s search now matches its analytic gradient and reported Fm, minimising the full reduced-correlation residual including the diagonal (previously the search used only the off-diagonal residual). Results move little for well-supported solutions; over-factored models can show larger changes.

  • Squared multiple correlations that start the "paf", "ml", and "uls" estimators are now held within [0, 1], matching psych::smc(); results can change for indefinite or badly conditioned correlation matrices (e.g. unsmoothed bootstrap resamples).

  • estimate_control() gains fiml_max_iter and fiml_tol to govern the FIML two-stage EM algorithm.

  • An efa_fit() fit with cor_method = "fiml" now returns a fiml component (convergence state, iteration count, missing-data patterns, sample size), and warns (efa_fiml_em_nonconvergence) when the EM algorithm does not converge.

  • A cor_method = "fiml" fit that cannot form the corrected two-stage chi-square now records chi_scaled_type = "uncorrected.lrt" and warns (efa_fiml_uncorrected_chisq).

  • An RMSEA confidence interval that cannot be computed is now reported as NA instead of stopping the fit.

Ordinal Correlations

  • Perfectly ordered polychoric or tetrachoric pairs are now reported as 0.9999/-0.9999, with a warning listing the affected pairs.

  • Binary pairs with an empty response cell now use a margin-preserving 0.5 continuity correction.

  • Unavailable asymptotic variances for polychoric/tetrachoric pairs are now reported as NA; DWLS stops with a clear error, and affected robust standard errors are withheld.

  • Rare response combinations in strongly correlated pairs are now handled more accurately, so such pairs less often block DWLS estimation or robust standard errors.

Power Analysis

  • A sample size solved by efa_power() with group > 1 is now a multiple of group, so N_per_group is a whole number (previously, e.g., a required total of 259 across two groups gave 129.5 per group).

  • In simulation mode, efa_power() now reports an NA hit rate when a requested factor-retention criterion never produces a suggestion.

  • Simulation mode now gives clear errors for missing or invalid N and n_datasets.

  • In simulation mode, efa_power() now rejects a p that disagrees with the population model (previously replaced silently).

  • In simulation mode, efa_power() now records the failure reason in replicates$fit_error when fits that recover the model fail (previously NA with no explanation).

Printed Output

  • Truncated variable names in loading tables no longer collide: names that would collide are abbreviated and numbered so they stay distinguishable.

  • print()/format() for Schmid-Leiman loading matrices now honour max_name_length, name_style, sort_loadings, and max_factors_per_block (previously accepted but ignored).

  • Errors from argument checks now carry the condition class efa_invalid_argument and name the function called.

Reliability and Factor Scores

  • The whole-scale row of an efa_reliability() table for a correlated-factors solution is now labelled factor = "total", level = "total" (previously "g"/"general", which implied a general factor such solutions do not have). Solutions with an actual general factor, and single-factor solutions, are unchanged.

  • efa_reliability() and OMEGA() now compute every omega total from the model-implied common variance, counting contributions from cross-loadings; subscale totals change for solutions with cross-loadings.

  • With variance = "correlation", the whole-scale omega total is now the model-implied common variance over observed total variance (previously total variance minus unique variances).

  • With variance = "sums_load", a subscale composite’s model-implied variance now also includes what it receives from other group factors, and this setting now applies to solutions without a general factor, including correlated-factors efa_fit() solutions (previously silently overridden to "correlation").

  • A Phi supplied to efa_reliability() without a pattern is now treated as the group-factor correlation matrix of s_load and enters the coefficients.

  • A Phi supplied together with a loading matrix of two or more factors is no longer dropped: the pair is now scored as a correlated-factors solution regardless of the matrix’s class (previously a hierarchy’s coefficients were returned).

  • efa_reliability() now refuses a Phi supplied together with an efa_schmid_leiman() loading table (previously dropped silently), since such a table already states it is a hierarchy.

  • efa_reliability() and OMEGA() now include a lavaan fit’s residual covariances in the model-implied composite variances; a freed residual covariance previously overstated omegas and understated standardized alpha.

  • efa_reliability() and OMEGA() now reject a bifactor or second-order lavaan fit whose latent variables are correlated (previously scored as though uncorrelated).

  • efa_reliability() now correctly scores an efa_schmid_leiman() loading table as a bifactor matrix, and no longer misreads an oblique solution’s pattern matrix as one (pass the efa_fit() object itself for that solution).

  • efa_reliability() now scores a lavaan fit whose variables each load on a single factor as the correlated-factors solution it is.

  • efa_reliability() now returns omega total, standardized alpha, and the H index for a single-factor solution consistently across all input routes, including a one-factor efa_fit() solution or a one-column loading matrix (previously refused).

  • The one factor of a single-factor solution is now reported under its input name, or "F1" if unnamed (previously always "g").

  • A cormat supplied in a different variable order than the solution is now reordered to match it (previously gave wrong subscale coefficients).

  • efa_schmid_leiman() and SL() now reorder a supplied Phi to match the loading columns (a differently ordered named Phi previously gave a silently wrong solution) and error if Phi does not match.

  • efa_scores() and FACTOR_SCORES() now check and reorder a named Phi and scoring correlation matrix (rho) to match the model, instead of silently using a mismatched order.

  • efa_scores() and FACTOR_SCORES() now check a correlation matrix supplied in x against the model and align it to the model variables, as already done for raw data; a mismatched matrix is now an error (previously returned the fitted solution’s weights regardless of the matrix supplied).

  • efa_scores()/FACTOR_SCORES() with method = "Bartlett" or "Anderson" now stop on a solution with a communality at or above 1, instead of returning unusable weights with a warning.

  • A factor_map (and OMEGA()’s factor_corres) must now hold only 0 and 1, and is checked against the loading matrix’s dimensions (previously any value was accepted and silently multiplied into the coefficients).

  • efa_schmid_leiman() and SL() now reject a solution with a single first-order factor.

  • efa_scores() and FACTOR_SCORES() now reject scoring data where a model variable is constant, infinite, or observed fewer than twice (previously produced NaN scores).

  • efa_scores() now reports the count of scored cases in settings$n_scored.

  • efa_schmid_leiman() and SL() now reject the unsupported arguments se, b_boot, ci, and seed.

Rotation

  • Oblique rotations now refuse a nearly singular transformation matrix (smallest singular value below 0.0001) at every evaluation point, including the oblique Procrustes solver; results can differ for near-degenerate solutions (typically more factors than the data support). Well-conditioned solutions are unaffected.

  • A gradient-projection rotation is now reported as converged only when the projected gradient meets tolerance (except simplimax, whose kinked objective still uses a stalled-progress criterion); convergence flags and diagnostics can differ from before for the same data.

  • Rotation criterion parameters from rotate_control() (gam, delta, maxit, simplimax k) are now validated before rotation runs, rejecting invalid values that previously reached the rotation engine silently or with an opaque error.

  • efa_procrustes() now refuses an S that is not crossprod(A) (previously a mismatched matrix silently minimised a different criterion), and refuses a badly conditioned T_init, not only a singular one.

  • varimax/promax with varimax_type = "svd" and Kaiser normalisation no longer fail on a solution containing a zero-communality variable.

Standard Errors

  • efa_fit() now rejects b_boot below 2 (error efa_b_boot_too_small) and ci of 0 or 1 (error efa_ci_out_of_bounds).

  • efa_fit(se = "sandwich") now withholds standard errors and confidence intervals when a Heywood case occurs.

  • Bootstrap output now reports the number of usable replicates when fewer than requested are available (for both single fits and pooled efa_mi() fits).

  • A bootstrap replicate whose rotation cannot be aligned now warns under the classed condition efa_boot_rotation_failed.

  • Analytic standard-error output now includes factor names in SE$Phi and variable-factor labels in vcov_unrot_loadings.

EFAtools 1.0.0

CRAN release: 2026-07-23

New Interface

  • The recommended interface now consists of lowercase efa_* functions, with estimation and rotation settings defined through estimate_control() and rotate_control().

  • The existing uppercase functions remain available without warnings, so current scripts continue to work, but new features will generally be added only to the efa_* interface.

  • Estimators, rotations, retention criteria, scoring methods, and presets can now be specified without matching capitalization exactly, while results continue to store and display the standard spelling.

Changes When Using the efa_* Interface

  • The efa_* functions now report an error for unused, misspelled, or misplaced arguments instead of silently ignoring them.

  • Estimation and rotation options such as convergence settings, iteration limits, and rotation parameters should now be supplied through estimate_control() or rotate_control().

  • Factor-retention functions always fit unrotated models and therefore reject rotation settings that would not affect the retention result.

  • The estimator argument is now named estimator rather than method, and rotation arguments use the names p_type and random_starts rather than P_type and randomStarts.

  • efa_retain() requires the full argument name max_iter_CD, because the shorter form max_iter is no longer matched automatically.

  • The second-order estimation preset for efa_schmid_leiman() is now specified with estimate_control(type = ...).

  • Control objects are validated when they are created, allowing invalid settings to be detected before a model is fitted.

Reproducibility

  • A supplied seed now controls the complete analysis, including random rotation starts, bootstrap samples, group-specific fits, and model averaging where applicable.

  • efa_average() now has a seed argument and produces the same result regardless of the number of parallel workers.

  • Seeded results from efa_parallel() and efa_hull() no longer depend on the parallel-processing setup.

  • Because random-number handling and several calculations were corrected, the same seed may produce different results than in earlier versions for efa_parallel(), efa_hull(), efa_average(), efa_group(), and some efa_simulate() settings.

Standard Errors and Fit Statistics

  • efa_fit(se = "information") now returns NA with an efa_se_unreliable warning when standard errors for unrotated loadings cannot be estimated reliably, such as for Heywood cases or poorly distinguished factors.

  • Information-based standard errors are now calculated for correlation matrices rather than covariance matrices, improving their accuracy and agreement with sandwich and bootstrap estimates.

  • Information-based standard errors are now supported only with Pearson correlations and FIML; use se = "sandwich" for polychoric, tetrachoric, or rank correlations.

  • FIML-based analytic standard errors now use the same small-sample scaling as the package’s other standard errors and fit statistics.

  • efa_mi() no longer reports AIC, BIC, or ECVI when these measures are not meaningful for the underlying fitted models.

Multiple Imputation and Group Comparisons

  • The pooled unrotated solution from efa_mi() no longer depends on the order of the imputed datasets.

  • Pooled unrotated loadings from efa_mi() now use the same orientation conventions as a regular efa_fit() result, making the two easier to compare.

  • These changes can affect unrotated loadings, their standard errors, and statistics derived from them, but rotated solutions and their fit indices remain unchanged.

  • Consensus results from efa_group() no longer depend on the order in which groups are supplied, so congruences, loading differences, salience classifications, and invariance conclusions are now stable across group orderings.

Factor Retention and Rotation

  • Fixed a regression in the revised MAP criterion, which could previously suggest an incorrect number of factors.

  • efa_smt() now rejects polychoric and tetrachoric correlations because its tests and fit measures are not valid for these inputs.

  • Rotated factors are now labelled F1, F2, and so forth according to the variance they explain, rather than inheriting labels that could change with the random seed.

  • Rotation diagnostics now distinguish between the total number of starting solutions and the number that were fully optimized; code using n_starts should use n_starts_total or n_optimized instead.

  • Criterion-based rotations now respect the specified maxit limit during every stage of the optimization.

Model Averaging

  • efa_average() no longer prints NaN% when no models converge and now clearly states which fitted models are included in each reported rate.

  • Named presets such as "SPSS", "psych", and "EFAtools" now use their intended iteration limits, so models that exceed those limits are marked as non-converged and excluded from the average.

Data Simulation

  • Cudeck–Browne model-error simulations are now reproducible across computing platforms and numerical libraries, although a given seed will produce different populations than in earlier versions.

  • With marginals = "VM", categorized data now reproduce the requested category proportions more accurately, with a warning when the requested thresholds cannot be transformed safely.

  • The documentation now clarifies that match = "thresholds" and match = "polychoric" produce the same data for normal marginals, and the selected value is now stored in the result settings.

  • The documentation now explains the missing-data mechanism produced by missing = "MAR" and the residual bias it may create for analyses based only on the observed data.

  • The documentation now explains that model_error = "WB" generally produces a larger RMSEA for the generating model than the requested target.

Input Validation and Correlation Handling

  • efa_screen() now handles collinear or nearly collinear variables without failing during its outlier checks.

  • Polychoric covariance calculations now handle sparse or nearly perfectly associated response tables more consistently, allowing DWLS analyses to continue while warning about problematic variable pairs.

  • Covariance matrices supplied where raw data or a correlation matrix is expected are now rejected with a message suggesting cov2cor().

  • Polychoric and tetrachoric correlations now require ordered factors or numeric response codes, preventing character or unordered-factor levels from being interpreted in alphabetical order.

Power Analysis and Factor Scores

  • efa_power() now clearly documents that N is the total sample size across groups and returns the corresponding sample size per group in N_per_group.

  • Simulation-based power summaries now distinguish structure-recovery rates from the underlying Tucker congruence values.

  • The documentation for FACTOR_SCORES() now correctly describes R2 as squared factor-score determinacy.

EFAtools 0.8.0

CRAN release: 2026-07-07

New Functions

  • efa_group() performs multi-group EFA.
  • efa_power() to conduct power analysis (analytic power based on RMSEA and simulation based power analysis).
  • efa_reliability() to calculate various reliability indices.
  • efa_screen() to screen data for multivariate normality and suitability for factor analysis.
  • efa_simulate() to simulate data with various distributions and missing data mechanisms.

Changes to Functions

  • EFA():
    • gains cor_method = "fiml" for raw data with missing values.
    • gains se = "sandwich" and se = "information".
    • renamed the top-level fields boot.SE, boot.CI, and boot.arrays on EFA objects to SE, CI, and replicates, as the old names are no longer accurate given the additional SE implementations.
    • All criterion-based rotations are now computed by a built-in gradient-projection rotation engine instead of the GPArotation package. The rotated solutions are numerically equivalent but implemented in C++ for speed to allow high numbers of random starts.
    • The default number of random starts for the rotation in EFA() (randomStarts) has been raised from 10 to 100, making local minima less likely for the rotation criteria that are prone to them.
    • additionally reports the Tucker-Lewis index (TLI, also called the non-normed fit index), the expected cross-validation index (ECVI), and the standardized root mean square residual (SRMR)
    • gains a seed argument and now fits the non-parametric bootstrap replicates (se = "np-boot") in parallel across replicates via the future framework.
    • now has a summary() method that prints the full, detailed solution (loadings with communalities and uniquenesses, explained variances, fit indices, and residuals); print() now gives a more compact overview.
  • EFA_AVERAGE() gains cor_method = "fiml" (passed to EFA()).
  • EFA_POOLED():
    • now dispatches its multiple-imputation standard-error pooling automatically by the standard-error method of its component fits: Rubin’s rules for the information method (with Wald confidence intervals), the two-stage pooled-inputs (MI2S) approach for the sandwich method, and the existing bootstrap pooling for the non-parametric bootstrap method.
    • Renamed the top-level field boot.MI to MI.
    • now defaults to target_method = "first_target", which aligns every imputation to the first imputation’s rotated solution with a single Procrustes rotation. For orthogonal rotations this reaches the same pooled estimate as "consensus" with substantially less work. "consensus" (Generalized Procrustes Analysis of the imputation-specific rotated loadings) is still available as an opt-in for orthogonal rotations, and now raises a classed condition (efa_consensus_oblique_unsupported) when combined with an oblique rotation, because the iterated-oblique-Procrustes centroid can drift to degenerate targets.
  • cor_method now accepts "poly" and "tetra" to compute polychoric and tetrachoric correlations from raw ordinal (respectively binary) data, using a two-step estimator with no empty-cell continuity correction. Supported by EFA() (including its non-parametric bootstrap), EFA_AVERAGE(), the suitability tests KMO() and BARTLETT(), and the retention criteria EKC(), KGC(), MAP(), SCREE(), SMT(), and N_FACTORS(). The criteria that compare the data against simulated continuous reference data (CD(), PARALLEL(), NEST(), and HULL()) do not support "poly" / "tetra" and signal an error.
  • The factor-retention functions CD(), EKC(), HULL(), KGC(), MAP(), NEST(), PARALLEL(), SCREE(), and SMT() now return objects of a common efa_retention class with shared print() and plot() methods.
  • COMPARE() objects now have a plot() method that returns a ggplot object. The plot is no longer drawn as a side effect of print().
  • Console output (the print, summary, and format methods) is now produced with the cli package, and the messages, warnings, and errors emitted across the package are now S3-classed conditions, which makes them easier to handle programmatically.
  • EFA(), SL(), and EFA_AVERAGE() now accept method = "MINRES" as a synonym for method = "ULS". Minimum residual and unweighted least squares are two names for the same estimator and return identical results.

Bug Fixes

  • EFA() and EFA_POOLED(): The comparative fit index (CFI) now floors the model and baseline noncentralities at zero before taking their ratio (Bentler, 1990), so it is no longer deflated toward zero when the baseline (independence) model fits comparatively well. The value is unchanged for well-fitting models and now matches lavaan. CFI can change for solutions in which the baseline model does not misfit much.
  • EFA_POOLED(): Corrected and extended the pooled chi-square-based model-fit indices. The D2 average relative increase in variance is now centred on the mean of the square-root statistics (Li, Meng, Raghunathan & Rubin, 1991), removing a one-sided inflation of the pooled chi-square, RMSEA, and CFI. Bootstrap/MI confidence intervals for the pooled fit indices are now the Rubin-Wald multiple-imputation summaries; a miscalibrated bootstrap-percentile interval (obtained by re-running the pooling algorithm over matched replicate indices) was removed, as was a mislabeled pooled Fm.
  • EFA_AVERAGE(): When every averaged solution fails (all runs error, fail to converge, or are Heywood cases), the function now returns an empty (NA) averaged result instead of erroring or averaging an empty set.
  • EFA() with method = "PAF": the reported number of iterations (iter) is now the number of iterations actually performed; it was previously one too high. The loadings, communalities, and convergence status are unchanged.
  • ULS extraction: the minimised objective is now the sum of squared off-diagonal residuals, consistent with its analytic gradient and the reported minimum (Fm). The diagonal residuals were previously included in the objective but not in its gradient. The fitted loadings are unchanged to within optimiser tolerance.
  • NEST() and PARALLEL(): A failed eigendecomposition of a degenerate simulated matrix now raises a clear error instead of resulting in undefined behaviour.
  • The chi-square model-fit statistic is now the Bartlett-corrected maximum likelihood discrepancy evaluated at the model-implied correlation matrix, for both ML and ULS extraction. For method = "ML" this matches stats::factanal() and psych::fa(); the small-sample Bartlett correction was previously omitted. For method = "ULS" it is now a proper chi-square-distributed statistic matching psych::fa(fm = "minres"); previously the least-squares residual sum of squares was multiplied by N - 1 and read as if it were Wishart-distributed, which produced an invalid p-value. The independence (baseline) model used for the CFI is rescaled onto the same discrepancy scale. Consequently the p-value, CFI, RMSEA (and its confidence interval), AIC, and BIC change for ML and ULS solutions, and the number of factors suggested by SMT() and HULL() may change for these methods.
  • PARALLEL(): The percentile reference eigenvalues are now computed with stats::quantile(), correcting a slight off-by-one in the previous indexing.
  • EFA(): For oblique rotations, the factor intercorrelations (Phi), the structure matrix, and the explained variances are now reflected and reordered consistently with the rotated loadings. Previously, when a factor was reflected to a positive orientation the factor intercorrelations were not sign-adjusted (so the structure matrix and reported correlations did not match the loadings), and with order_type = "ss_factors" the factor intercorrelations were not reordered at all.
  • EFA(): The returned rotation matrix (rotmat) is now reflected and reordered consistently with the rotated loadings, for both orthogonal and oblique rotations, so that the rotated loadings can be reconstructed from the unrotated loadings and rotmat. Previously the sign reflection was not applied to it and its factors were left in a different order, so this reconstruction did not hold whenever a factor was reflected or reordered.
  • HULL(): The convex-hull elimination now tests every triplet of adjacent solutions, including the one formed by the last interior solution. Previously this final triplet was skipped, so a solution lying below the line connecting its neighbours could remain on the hull. This can change the number of factors suggested by HULL() (and hence by N_FACTORS()).
  • NEST(): When the test accepts every eigenvalue it examines (no empirical eigenvalue falls at or below its reference within the search range), it now retains that last accepted model rather than the model with one fewer factor. The search range is also bounded so that the reference model fitted at each step stays over-identified.
  • PARALLEL(): When every real eigenvalue exceeds its reference, so the decision rule finds no crossing, the suggested number of factors is now all retained components, reported with a warning, instead of a silent NA (matching the convention used by EKC()).
  • EFA(se = "np-boot"): The non-parametric bootstrap no longer repeats per-replicate warnings (about arguments pinned alongside type, and about the iterative fit reaching its maximum number of iterations) once per replicate. They are now suppressed during resampling, and non-convergence across replicates is reported once as a summary.
  • COMPARE(): With reorder = "congruence", the columns of y are now matched to those of x by an optimal one-to-one assignment that maximizes the total Tucker congruence, rather than by an independent per-factor best match. The greedy match could assign two factors of x to the same column of y, duplicating that column and dropping another, which corrupted the reported differences, factor correspondences, and root mean squared distance. The result is unchanged whenever the greedy match was already one-to-one.

EFAtools 0.7.1

CRAN release: 2026-05-08

Changes to Functions

  • EFA(): Added randomStarts argument passed to GPArotation functions, as suggested by Coen Bernaards.
  • FACTOR_SCORES(): Added rho argument, thanks to Andreas Soteriades.
  • EFA_POOLED(): Fixed issue that could lead to averaged Phi not being symmetric.

EFAtools 0.7.0

CRAN release: 2026-04-30

New Functions

  • MAP() computes the Velicer MAP criterion (both TR2 and TR4).
  • PROCRUSTES() to perform orthogonal and oblique Procrustes / target rotation.
  • CONSENSUS_PROCRUSTES() to perform Procrustes on a list of targets to obtain a common target.
  • residuals.EFA() to extract and print residuals and, if computed, standardized residuals.
  • EFA_POOLED() to run EFA on a list of multiple imputated datasets and pool the results. Thanks to Andreas Soteriades for the suggestion and first implementation.
  • print.EFA_POOLED(), print method adapted from print.EFA()

Changes to Functions

  • N_FACTORS() can also compute the MAP criterion.
  • EFA():
    • Now returns and prints residuals and, if SEs are computed, standardized residuals.
    • Calculates and prints RMSR.
    • Can calculate bootstrap standard errors and CIs of parameters and fit indices.
  • print.EFA() now prints more information.

Bug Fixes

  • Fixed a bug in OMEGA() that led to incorrect omega, H, and ECV values for lavaan bifactor models. Tnanks to Christopher D. King for bug report and suggested fix.
  • Small fix in the documentation of EFA_AVERAGE().
  • Fixed incorrect calculation of RMSEA in EFA().

EFAtools 0.6.1

CRAN release: 2025-07-30

Changes to Functions

  • EKC(): Now correctly returns the number of factors based on the first time the eigenvalues drop below the references values.

EFAtools 0.6.0

CRAN release: 2025-06-19

New Functions

  • Added NEST() to perform the Next Eigenvalue Sufficiency Test (Achim, 2017).

Changes to Functions

  • EKC(): The implementation based on Auerswald and Moshagen (2019) used in previous versions differed from the original implementation by Braeken and van Assen (2017). Now both versions are implemented and can be selected with the new type argument. Thanks to Luis Eduardo Garrido for pointing this out and to Johan Braeken for sharing sample code, based on which the original version is now implemented.
  • N_FACTORS():
    • Updated default settings to only use often used and well performing factor retention methods (others, like the Kaiser Guttman criterion can still be used).
    • Added NEST as additional factor retention method.
    • New arguments: ekc_type, alpha_nest, and n_datasets_nest to account for the changes in EKC() and to control NEST().

EFAtools 0.5.0

CRAN release: 2025-05-23

Changes to Functions

  • print.EFA():
    • Now returns explained variance from rotated, rather than unrotated solution, if a rotation was performed.
    • Now prints communalities and uniquenesses in loading/pattern matrix
  • EFA(): Calculate and return model implied correlation matrix.

EFAtools 0.4.6

CRAN release: 2025-03-21

General

  • Small change in test of .gof(): Changed some tests to take care of the ATLAS issue when using R-devel on x86_64 Fedora 34 Linux with alternative BLAS/LAPACK.

EFAtools 0.4.5

CRAN release: 2024-12-22

Bug Fixes

EFAtools 0.4.4

CRAN release: 2023-01-06

Changes to Functions

  • print.EFA(): Added arguments cutoff, digits and max_name_length that are passed to print.LOADINGS().
  • print.LOADINGS(): New Argument max_name_length to control the maximum length of the displayed variable names (names longer than this will be cut on the right side). Previously, this was fixed to 10 (which is now the default).

Misc

  • Updated a test of a helper function (.gof()) that threw an error when using R-devel on x86_64 Fedora 36 Linux with alternative BLAS/LAPACK.
  • added dontrun to examples of EFA_AVERAGE() and its print and plot methods as these were causing issues on R-oldrel which were not directly related to EFAtools and thus could not be fixed from within the package.

EFAtools 0.4.3

CRAN release: 2022-10-02

Changes to Functions

  • .gof(): Changed the helper function to take care of the MKL issue when using R-devel on x86_64 Fedora 34 Linux with alternative BLAS/LAPACK.

EFAtools 0.4.2

CRAN release: 2022-09-27

Changes to Functions

  • .is_cormat(): Changed the helper function to better detect wheter a matrix is a correlation matrix.
  • PARALLEL(): Added a check, testing whether N > n_vars and throw an error if this is not the case.

Bug Fixes

  • Fixed some tests due to upcoming changes in the psych package which EFAtools depends on.

EFAtools 0.4.1

CRAN release: 2022-04-24

Bug Fixes

  • Minor fixes in tests to solve problems on macOS m1.

EFAtools 0.4.0

CRAN release: 2022-03-21

Changes to Functions

  • EFA(): Changed error to warning when model is underidentified. This allows the Schmid-Leiman transformation to be performed on a two-factor solution.
  • OMEGA(): Added calculation of additional indices of interpretive relevance (H index, explained common variance [ECV], and percent of uncontaminated correlations [PUC]). This is optional and can be avoided by setting add_ind = FALSE.

Bug Fixes

  • CD(): Added na.omit() to remove missing values from raw data to avoid an error in the comparison-data procedure.

EFAtools 0.3.1

CRAN release: 2021-03-27

General

  • When testing for whether a matrix is singular and thus smoothing should be done, test against .Machine$double.eps^.6 instead of 0, as suggested by Florian Scharf.

Changes to Functions

  • EFA():
    • Added warnings if type = "SPSS" was used with method = "ML" or method = "ULS", or with a rotation other than none, varimax or promax.
    • Avoided smoothing of non-positive definite correlation matrices if type = "SPSS" is used.
    • Use Moore-Penrose Pseudo Inverse in computation of SMCs if type = "psych" is used, by calling psych::smc().
    • Use varimax_type = "kaiser" if type = "EFAtools" is used with varimax or promax.

Bug Fixes

  • EFA_AVERAGE():
    • Added future.seed = TRUE to call to future.apply::future_lapply() to prevent warnings.
    • Fixed test for Heywood cases from testing whether a communality or loading is greater than .998, to only test whether communalities exceed 1 + .Machine$double.eps
  • print.EFA(): Fixed test for Heywood cases from testing whether a communality or loading is greater than .998, to only test whether communalities exceed 1 + .Machine$double.eps
  • OMEGA(): Small bugfix when lavaan second-order model is given as input

EFAtools 0.3.0

CRAN release: 2020-11-04

General

  • Added examples for EFA_AVERAGE() to readme and the EFAtools vignette
  • Updated examples in readme and vignettes according to the updated OMEGA function

New Functions

  • Added function EFA_AVERAGE() and respective print and plot methods, to allow running many EFAs across different implementations to obtain an average solution and test the stability of the results.

Changes to Functions

  • EFA(): Defaults that were previously set to NULL are now mostly set to NA. This was necessary for EFA_AVERAGE() to work correctly.
  • PARALLEL(): Rewrote the generation of random data based eigenvalues to be more stable when SMCs are used.
  • OMEGA(): Changed expected input for argument factor_corres from vector to matrix. Can now be a logical matrix or a numeric matrix with 0’s and 1’s of the same dimensions as the matrix of group factor loadings. This is more flexible and allows for cross-loadings.

EFAtools 0.2.0

CRAN release: 2020-09-17

General

  • Created new vignette Replicate_SPSS_psych to show replication of original psych and SPSS EFA solutions with EFAtools.

New Functions

  • Added function FACTOR_SCORES() to calculate factor scores from a solution from EFA(). This is just a wrapper for the psych::factor.scores function.
  • Added function SCREE() that does a scree plot. Also added respective print and plot methods.

Changes to Functions

  • CD(): Added check for whether entered data is a tibble, and if so, convert to vanilla data.frame to avoid breaking the procedure.
  • EFA():
    • Updated the EFAtools type in PAF and Promax.
    • Added p value for chi square value in output (calculated for ML and ULS fitting methods).
    • Updated the SPSS varimax implementation to fit SPSS results more closely.
    • Created an argument “varimax_type” that is set according to the specified type, but that can also be specified individually. With type R psych and EFAtools, the stats::varimax is called by default (varimax_type = "svd"), with type SPSS, the reproduced SPSS varimax implementation is used (varimax_type = "kaiser").
    • Renamed the kaiser argument (controls if a Kaiser normalization is done or not) into normalize to avoid confusion with the varimax_type argument specifications.
  • ML(): Changed default start method to “psych”.
  • N_FACTORS():
    • Added option to do a scree plot if “SCREE” is included in the criteria argument.
    • Added a progress bar.
  • OMEGA(): Now also works with a lavaan second-order solution as input. In this case, it does a Schmid-Leiman transformation based on the first- and second-order loadings first and computes omegas based on this Schmid-Leiman solution.
  • SL(): Now also works with a lavaan second-order solution as input (first- and second-order loadings taken directly from lavaan output).

Bug Fixes

  • .get_compare_matrix(): Fixed a bug that occurred when names of data were longer than n_char
  • COMPARE(): Fixed a bug that occurred when using reorder = "names".
  • EFA(): RMSEA is now set to 1 if it is > 1.
  • HULL(): Fixed a bug that occurred when no factors are located on the HULL
  • KMO(): Fixed a bug that the inverse of the correlation matrix was not taken anew after smoothing was necessary.
  • PARALLEL():
    • Fixed a bug that occurred when using decision_rule = "percentile"
    • Relocated error messages that were not evaluated if no data were entered (and should be)
  • print.COMPARE(): Fixed a bug that occurred when using print_diff = FALSE in COMPARE().
  • print.KMO(): Fixed a bug that printed two statements instead of one, when the KMO value was < .6.

Minor Changes

  • OMEGA() and SL(): Added an error message if the entered term in g_name is invalid (i.e., it cannnot be found among the factor names of the entered lavaan solution).

EFAtools 0.1.1

CRAN release: 2020-07-13

Minor Changes

  • Added an error message in PARALLEL() if no solution has been found after 25 tries.

Bug Fixes

  • Updated different tests

  • Deleted no longer used packages from Imports and Suggests in DESCRIPTION

  • PARALLEL(): fixed a bug in indexing if method "EFA" was used.

EFAtools 0.1.0

CRAN release: 2020-07-07

  • Added a NEWS.md file to track changes to the package.
  • Initial CRAN submission