Changes in version 1.0.1 (2026-10-15)                  

First version submitted to CRAN.

  - plot_networks_by_group() restores par() with an immediate on.exit()
    call and always closes its temporary device; the NetPowerLab app
    restores the user's par() after the plot.

  - The SemPowerLab application (run_sempowerlab()), added during
    development, moved to the PsyMetricTools package, which covers
    latent variable models. semPower is no longer suggested.

New functions

  - New run_netpowerlab(): launches the NetPowerLab Shiny application,
    which plans the sample size of a network study by wrapping
    bootnet::netSimulator() and powerly::powerly(). The reference
    network is built block by block (one block per instrument) and acts
    as the effect size; the app also writes the reproducible script and
    a draft of the Participants section. shiny, bslib and powerly moved
    into Suggests.
  - New sample_size_EGA_montecarlo(), with sample_size_EGA_control(),
    make_likert_thresholds(), plot_sample_size_EGA_montecarlo(),
    plot_power_curve_ci(), plot_failure_decomposition(),
    show_recommendation() and show_plots(): a priori sample size for EGA
    through Monte Carlo simulation.

Bug fixes

  - NetPowerLab ran powerly with an increasing curve for specificity,
    which falls as N grows. It now reports a recommendation only when
    powerly converged and the value is not at the top of the range, and
    the script and paragraph follow the network and settings actually
    simulated.
  - sample_size_EGA_control() merged nested lists: control =
    list(targets = list(ari = .80)) silently kept the default
    edge_correlation target. Entries now replace the defaults.
  - sample_size_EGA_montecarlo(): items that EGA leaves without a
    community no longer inflate the ARI; bootstrap resamples that never
    reach the target are treated as censored instead of dropped, so the
    upper CI bound is no longer understated; the bootstrap CI is
    reproducible with parallel = TRUE; a non positive definite partial
    network stops with a message that gives the admissible edge weight;
    the sensitivity sweep skips the edge-threshold runs when no target
    depends on them and compares every row with the main run.
  - optimize_leiden_resolution() treated untested resolutions (after an
    early stop) as having TEFI = 0, could choose one of them as the
    best, and misaligned the models with their rows in comparison when a
    resolution failed.
  - plot_lvm() labelled residual correlations as residual partial
    correlations, and did not restore the graphical parameters.
  - plot_latent_network_diagram() called getmatrix() without importing
    it, so psychonetrics models failed unless psychonetrics was
    attached.
  - compute_netScores() wrote to the global environment when the
    stability step failed.
  - convert_EGA_to_df() returned NA as the algorithm for EGAnet 2.x
    results.
  - mgm_error_metrics() passed levels instead of level to mgm::mgm().

CRAN compliance

  - No function assigns into the global environment: the print methods
    for silent_gg and silent_plot are now registered by the package.
  - Nothing is written to disk by default: plot_latent_network_diagram()
    requires output_path.
  - Functions report progress with message() instead of cat(), and
    compute_netScores() only asks for dimension names in interactive
    sessions.
  - No fixed seeds inside functions: boot_and_evaluate(),
    refine_items_by_stability() and compute_netScores() default to seed
    = NULL; the user's future plan is restored on exit; parallel
    defaults use at most 2 cores.
  - Every exported function documents its return value and has a
    runnable example.
  - reshape2 and stringr are no longer needed; viridis moved to
    Suggests; psychonetrics added to Suggests. Requires R >= 4.1.0.

                        Changes in version 1.0.0                        

  - Initial release (GitHub only).
  - 40 functions for network analysis and psychometric modeling.
  - Tools for centrality calculation, bridge metrics, and community
    structure visualization.
  - Support for group network comparison and Mixed Graphical Models
    (MGM).
  - Exploratory Graph Analysis (EGA) integration via EGAnet.
  - Bootstrap evaluation of network estimation algorithms.
  - Advanced visualization functions for network plots with R2 rings,
    Cleveland dot plots, and bullet charts.