Reproduces classic Rasch psychometric analysis features
    using Bayesian item response theory models fitted with 'brms' following 
    Bürkner (2021) <doi:10.18637/jss.v100.i05> and Bürkner (2020) <doi:10.3390/jintelligence8010005>.
    Supports both dichotomous and polytomous Rasch models. 
    Features include posterior predictive item fit, conditional infit, item-restscore
    associations, person fit, differential item functioning, local dependence
    assessment via Q3 residual correlations, dimensionality assessment with
    residual principal components analysis, person-item targeting plots,
    item category probability curves, and reliability using relative
    measurement uncertainty following Bignardi et al. (2025) <doi:10.31234/osf.io/h54k8_v1>.

Generate citations and references for R packages from CRAN or Bioconductor.
    Supports RIS and BibTeX formats with automatic DOI retrieval from GitHub repositories
    and published papers. Includes command-line interface for batch processing.

Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models.

Provides a simple interface to search and retrieve scientific articles
    from the 'SciELO' (Scientific Electronic Library Online) database <https://scielo.org>.
    It allows querying, filtering, and visualizing results in an interactive table.

The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protect_linked_tables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The suppression method Gauss (default) is implemented independently of 'sdcTable'.
 The function, PTgui(), starts a graphical user interface based on the 'shiny' package.

Provides automated statistical analysis, rich visualization, and
    multi-format narrative reporting through a unified pipeline. Descriptive
    statistics are available via easy_describe() and easy_group_summary().
    Inferential tests with plain-language narratives are provided by
    easy_regression(), easy_logistic_regression(), easy_ttest(), easy_anova(),
    easy_chisq(), easy_ztest(), easy_ftest(), easy_correlation(), easy_wilcox(),
    and easy_kruskal(). Publication-ready 'ggplot2' visualizations are produced
    by easy_histogram(), easy_boxplot(), easy_scatter(), easy_barplot(),
    easy_qqplot(), easy_density(), easy_correlation_heatmap(),
    easy_regression_diagnostics(), and easy_odds_ratio_plot(). The core
    Narrative Generator Module applies conditional logic to extracted p-values,
    effect sizes, and model-fit metrics to produce statistically sound,
    human-readable explanations automatically. Results render in the 'RStudio'
    Viewer (HTML), the console (ASCII), or export directly to Microsoft Word
    via 'flextable' and 'officer'.