Changes in version 1.2.3                        

New features

  - New run_sempowerlab(): launches the SemPowerLab Shiny application,
    which plans the sample size of a predictive design with latent
    variables (two correlated latent predictors and a latent criterion)
    by wrapping semPower::semPower.powerRegression(). It draws the
    model, plots the power curve, crosses the effect with the average
    factor loading, and writes the reproducible script and a draft of
    the Participants section. The application moves here from the
    InterconectaR package. shiny, bslib and semPower added to Suggests.
  - The application passes the criterion as the first factor, as
    semPower expects, and every output (cards, curve, script and
    paragraph) follows the specification that produced the result rather
    than inputs edited afterwards.

Bug fixes

  - boot_cfa() now returns the scaled (robust) fit indices when the
    model is fitted with a robust test statistic. Since lavaan 0.6-14
    the test option is a vector (c("standard", "scaled.shifted") for
    WLSMV), and the internal helpers is_robust_estimator_lavaan() and
    lavaan_estimator() inspected only its first element. That element is
    always "standard", so every robust fit was classified as non-robust
    and fit_measures1 carried cfi, tli and rmsea instead of cfi.scaled,
    tli.scaled and rmsea.scaled. With ordinal data the gap is not
    cosmetic: in a 23-item five-factor model the reported CFI was .988
    against a true robust value of .947, which made the bootstrap figure
    contradict the fit table. Both helpers now test every declared
    statistic with any(test_types %in% ...), the same way boot_efa()
    already did. As a side effect the estimator column now reports WLSMV
    instead of DWLS_variant.
  - The column names keep their current form (CFI, TLI, RMSEA, ...) so
    the plotting functions boot_cfa_plot(), boot_cfa_plot_enhanced(),
    boot_cfa_density(), boot_cfa_raincloud() and calculate_per_fit() are
    unaffected; only the values change.

                        Changes in version 1.2.1                        

CRAN fixes

  - The PDF manual now builds without LaTeX errors: the Unicode symbols
    >=, <=, -> and R^2 are written in portable form in the documentation
    of boot_cfa_plot_enhanced(), plot_mediation_chord(),
    plot_mediation_donuts() and plot_path_mediation(). This was the
    error that caused the 1.2.0 submission to be archived by CRAN.
  - DESCRIPTION now declares R (>= 4.1.0), required by the use of the
    native pipe |> / lambda \(...) syntax in invertir_items() and
    split_data_stratified_clustered().

Improvements

  - plot_path_cfa() rewritten: latent variables as circles, observed
    variables as rectangles, fan-out arrows, black-and-white default.

                        Changes in version 1.2.0                        

Improvements

  - plot_multi_sem() now supports multi-row panel layouts via the new
    nrow and ncol arguments. By default it keeps the previous behaviour
    (a single row with one column per model); when a grid is requested,
    leftover cells are left blank. Saved-figure dimensions scale with
    the grid (width_per per column, height per row).

                        Changes in version 1.0.0                        

  - Initial CRAN submission

Features

Data Preprocessing

  - filtrar_aberrantes() - Filter aberrant response patterns using
    Mahalanobis distance
  - invertir_items() - Reverse score items
  - split_data_two() / split_data_three() - Split data for
    cross-validation
  - smote_multiclass() - SMOTE oversampling for imbalanced multiclass
    data

Exploratory Factor Analysis (EFA)

  - EFA_modern() - Modern EFA with multiple rotation options
  - EFA_plot() - Visualize factor loadings
  - Standardized_solutions() - Extract standardized EFA solutions
  - boot_efa() - Bootstrap EFA for stability analysis
  - efa_with_bootstrap() - EFA with bootstrap resampling

Confirmatory Factor Analysis (CFA)

  - multi_cfa() - Fit multiple CFA models
  - Standardized_solutions_cfa() - Extract standardized CFA solutions
  - boot_cfa() - Bootstrap CFA for stability analysis
  - boot_cfa_stability() - CFA stability across sample sizes

Measurement Invariance

  - easy_invariance() - Measurement invariance analysis with ordinal
    data following Wu and Estabrook (2016)

Visualization

  - Plot_Likert() / Plot_Likert2() - Likert scale visualizations
  - boot_cfa_plot() - Bootstrap CFA results visualization
  - boot_cfa_density() - Bootstrap distribution density plots
  - boot_cfa_raincloud() - Raincloud plot visualizations
  - boot_efa_plot() / boot_efa_forest_plot() - Bootstrap EFA
    visualizations
  - plot_cfa_stability() family - CFA stability visualizations

Reliability

  - calcula_omega_mcdonald() - McDonald's omega coefficient

Utilities

  - save_to_excel_table() - Export results to Excel
  - create_groups() - Create item groups for factor definitions
  - Model syntax generators for lavaan