# EFAtools 1.1.0

## Comparing and Averaging Solutions

* `efa_compare()` now saves `print()`, `format()`, and `plot()` display settings, and reuses them.

* `efa_compare()`'s `are_equal` now returns `NA`, not `0`, when the compared values differ before the decimal point.

* `efa_compare()`'s elementwise differences are now signed.

* `efa_compare(reorder = "congruence")` now also matches the sign of single-factor solutions.

* Fixed `efa_compare()`'s handling of missing loadings. This previously inflated `diff_corres` and `diff_corres_cross`.

* Fixed a bug that could trigger spurious warnings from `efa_average()`'s progress bar.

## Data Screening and Simulation

* Fixed two bugs in `efa_screen()`'s outlier detection. Both could give an incorrect or less robust result.

* `efa_screen()`'s multicollinearity verdict is now based on the condition index alone, following Belsley (1991).

* `efa_screen()` now withholds an uninformative Henze-Zirkler p-value instead of reporting an exact 0 or 1.

* Mardia's skewness and kurtosis in `efa_screen()` now use small-sample corrections.

* `efa_simulate()` gains `missing_vars`, to restrict missingness to selected variables.

* Fixed `efa_simulate()`'s reported `population` under `marginals = "VM"` and `force_pd = TRUE`.

## Factor Retention

* The `type` argument of `efa_ekc()` and `ekc_type` of `efa_retain()` no longer have any effect: the empirical Kaiser criterion now always follows Braeken and van Assen (2017). Suggested factor counts can differ from earlier results.

* `efa_nest()` results can now be plotted.

* `efa_parallel()` now shares simulated reference data between `"PCA"` and `"SMC"`, roughly halving simulation time. Results for a given seed can differ from before.

## Input Validation and Correlation Handling

* `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.

* Correlation-matrix smoothing now consistently returns a positive-definite matrix.

## Missing Data and Multiple Imputation

* `efa_mi()`'s `rmsr_upper` argument is deprecated and no longer used.

* With `target_method = "consensus"`, `efa_mi()`'s pooled result no longer depends on the order of `data_list`. Results can differ from earlier versions.

* `efa_mi()`'s pooled RMSEA is now capped at 1, and its confidence interval is withheld when it excludes the point estimate.

* An `efa_mi()` solution now reports the Heywood status of each imputation, in `mi_admissibility`.

* A `cor_method = "fiml"` fit with analytic standard errors now withholds loading standard errors when the rotation is weakly determined, matching the other standard-error methods.

## Model Estimation

* The `"uls"` estimator now minimises the full residual, including the diagonal, matching its analytic gradient. Results can shift for over-factored models.

* Starting values for the `"paf"`, `"ml"`, and `"uls"` estimators are now kept within [0, 1], matching `psych::smc()`.

* `estimate_control()` gains `fiml_max_iter` and `fiml_tol`, to control the FIML algorithm.

* A `cor_method = "fiml"` fit now reports its convergence status in a new `fiml` component, and warns if it does not converge.

* 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.

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

* Unavailable asymptotic variances for polychoric or tetrachoric pairs are now reported as `NA`, and affected robust standard errors are withheld.

* Rare response patterns in strongly correlated pairs are now handled more accurately. They less often block `DWLS` or robust standard errors.

## Power Analysis

* `efa_power()` with `group > 1` now always solves for a whole-number sample size per group.

* In simulation mode, `efa_power()` now reports `NA`, not a misleading hit rate, when a retention criterion never gives a suggestion.

* In simulation mode, `efa_power()` now rejects a `p` that disagrees with the population model, instead of silently replacing it.

## Printed Output

* Truncated variable names that would collide in loading tables are now 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`.

## Reliability and Factor Scores

* `efa_reliability()` and `OMEGA()` now compute every omega total from the model's full implied common variance, including cross-loadings and contributions from other group factors. Results can change for solutions with cross-loadings.

* The whole-scale row of a correlated-factors solution is now labelled `"total"`, not `"g"`/`"general"`, which implied a general factor such solutions do not have.

* `efa_reliability()` now recognises several solution types correctly, including Schmid-Leiman tables and single-factor solutions, which were previously misclassified, mislabeled, or refused.

* `efa_scores()` and `FACTOR_SCORES()` with `method = "Bartlett"` or `"Anderson"` now stop on a communality at or above 1, instead of returning unusable weights.

* `efa_scores()` and `FACTOR_SCORES()` now reject scoring data with a constant, infinite, or too-short variable, instead of silently returning `NaN` scores.

## Rotation

* Oblique rotations, including `efa_procrustes()`, now refuse a nearly singular transformation matrix at every step. Results can differ for near-degenerate solutions.

* A gradient-projection rotation is now reported as converged only when its projected gradient meets tolerance. Convergence flags can differ from before.

## Standard Errors

* `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.

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

# EFAtools 1.0.0

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## 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

## Bug Fixes

* Updated `OMEGA()` to accommodate changes in the upcoming version of `psych::schmid()`

# EFAtools 0.4.4

## 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

## 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

## 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

## Bug Fixes

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


# EFAtools 0.4.0

## 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

## 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

## 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

## 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

## 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

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