Changelog • Saqrmisc

Saqrmisc 0.9.2

Added cluster() as a short alias for clustering().
Added optional cluster_* aliases for the existing clustering helpers. All original function names remain unchanged.
Fixed clustering() model comparisons by storing the scalar fitted log-likelihood instead of each model’s variable-length iteration history.
clustering() now stores a comparison table containing BIC, AIC, and ICL. plot(..., type = "all") plots each criterion separately, while "bic", "aic", and "icl" can be requested individually.
Information-criterion plots now place the number of clusters on the x-axis and draw one line per covariance model.
Corrected MoEClust information-criterion ranking so larger values select the best model.
Preserved both the complete input data and the complete-case analysis data in clustering results.
get_cluster_assignments() now preserves rows omitted during complete-case fitting and marks their assignments and probabilities as NA.
Added a tidy interface: summary() returns a ranked model tibble, fitted() returns original rows with fitted clusters, and get_best_model(..., what = "fit") extracts the raw MoEClust fit.
plot(..., type = "all") now means every plot for every fitted model. Added plot_best_model() and plot_model() for focused plotting.

mosaic_analysis() no longer draws the variable-name axis titles by default, which previously overprinted the category labels (especially next to thin categories). Set show_varnames = TRUE to restore them.

This is the initial release of the Saqrmisc package, providing comprehensive tools for data analysis and visualization.

Model-Based Clustering Analysis (run_full_moe_analysis)
- Comprehensive MoEClust analysis with all 14 covariance models
- Robust error handling for failed model convergence
- Dual-scale outputs (original and scaled data)
- Profile plots and summary tables
Statistical Comparison Analysis (generate_comparison_plots)
- Automated comparison plots using ggbetweenstats
- Support for stratified analysis by additional variables
- Quality control with automatic exclusion of small categories
- Flexible output options (plots and tables)
Categorical Variable Analysis (mosaic_analysis)
- Comprehensive mosaic plot analysis
- Chi-square testing with effect size calculation (Cramér’s V)
- Quality filtering for minimum observation counts
- Detailed summary tables with percentages
Helper Functions
- view_results() for easy visualization of clustering results

Core: MoEClust, mclust, dplyr, ggplot2, ggstatsplot, vcd, grid, tibble, rlang, gridExtra, gt, janitor, prcr, ggcharts, MASS, tidyverse
Suggested: testthat, knitr, rmarkdown, devtools, roxygen2