Package index • Saqrmisc

All functions

add_total(): Add Row Totals to Data Frame
assess_cluster_stability(): Assess Cluster Stability via Bootstrap
auto_describe(): Automatically Generate Descriptive Statistics for All Variables
categorical_table(): Generate Publication-Ready Frequency Tables for Categorical Variables
center(): Center Variables (Mean-Centering)
center_vec(): Center a Vector (for use in mutate/across)
clustering() cluster(): Model-Based Clustering Analysis
clustering_aliases cluster_fit cluster_models cluster_best cluster_compare cluster_compare_table cluster_assignments cluster_view cluster_plot_model cluster_plot_best cluster_stability cluster_report: Optional cluster_* aliases
compare_groups(): Generate Group Comparison Plots and Statistics
compare_models(): Compare Models by Information Criteria
compare_networks(): Compare Two Networks
convert_sequences(): Convert Sequence Data to Various Formats
correlation_matrix(): Create a Publication-Ready Correlation Matrix
correlations(): Full Pairwise Correlation Table
counts(): Count Events per ID
data_overview(): Quick Dataset Overview
descriptive_table(): Generate Publication-Ready Descriptive Statistics Table
estimate_grouped_networks(): Estimate Grouped Networks
estimate_single_network(): Estimate a Single Network
fitted(<moe_analysis>): Extract Fitted Clustering Data
generate_cluster_report(): Generate Interpretable Cluster Report
get_best_model(): Get Best Model
get_cluster_assignments(): Get Cluster Assignments with Original Data
is_outlier(): Check if Value is Outlier (vectorized for mutate/across)
launch_app(): Launch the Saqrmisc mosaic analysis Shiny app
list_models(): List Available Models
missing_analysis(): Analyze Missing Data Patterns
model_comparison_table(): Create Formatted Model Comparison Table
mosaic_analysis(): Perform a comprehensive mosaic plot analysis
normality_check(): Check Normality of Variables
outlier_check(): Check for Outliers
pass(): Pass R Output to AI for Interpretation
plot(<moe_analysis>): Plot method for moe_analysis objects
plot(<mosaic_analysis>): Plot method for mosaic_analysis objects
plot_best_model(): Plot the Best Fitted Clustering Model
plot_model(): Plot One Fitted Clustering Model
print(<cluster_stability>): Print method for cluster_stability
print(<grouped_network_analysis>): Print method for grouped_network_analysis objects
print(<html_table>): Print method for html tables
print(<latex_table>): Print method for latex tables
print(<markdown_table>): Print method for markdown tables
print(<moe_analysis>): Print method for moe_analysis objects
print(<mosaic_analysis>): Print method for mosaic_analysis objects
print(<network_analysis>): Print method for network_analysis objects
print(<saqr_result>): Print method for saqr_result
replace_missing(): Replace Missing Values
replace_outliers(): Replace Outliers
reverse_code(): Reverse Code Variables
reverse_code_vec(): Reverse Code a Vector (for use in mutate/across)
saqr_result(): Create a saqr_result object
scale_vars(): Scale Variables
scale_vec(): Scale a Vector (for use in mutate/across)
set_api_key(): Set API Key and Options for Session
set_claude_key(): Set Anthropic (Claude) API Key
set_gemini_key(): Set Google Gemini API Key
set_openai_key(): Set OpenAI API Key
set_openrouter_key(): Set OpenRouter API Key
standardize(): Standardize Variables (Z-Score)
standardize_vec(): Standardize a Vector (for use in mutate/across)
summary(<moe_analysis>): Summary method for moe_analysis objects
summary(<mosaic_analysis>): Summary method for mosaic_analysis objects
summary(<network_analysis>): Summary method for network_analysis objects
to_dataframe(): Convert Any Table to Data Frame
to_gt(): Convert Table to GT Format
to_html(): Convert Table to HTML Format
to_kable(): Convert Table to Kable Format
to_latex(): Convert Table to LaTeX Format
to_markdown(): Convert Table to Markdown Format
view_results(): View Results from MoEClust Analysis (Deprecated)
winsorize_vec(): Winsorize a Vector (vectorized for mutate/across)