Package: Saqrmisc 0.9.8

Mohammed Saqr

Saqrmisc: Miscellaneous Functions for Data Processing and Analysis

A comprehensive R package providing miscellaneous functions for data processing, statistical analysis, and visualization. Includes functions for latent profile clustering via the 'latents' package, comparison plots with statistical testing, and mosaic plot analysis for categorical variables. The package offers robust error handling, comprehensive documentation, and user-friendly interfaces for complex statistical tasks.

Authors:Mohammed Saqr [aut, cre]

Saqrmisc_0.9.8.tar.gz
Saqrmisc_0.9.8.zip(r-4.7-any)Saqrmisc_0.9.8.zip(r-4.6-any)Saqrmisc_0.9.8.zip(r-4.5-any)
Saqrmisc_0.9.8.tgz(r-4.6-any)Saqrmisc_0.9.8.tgz(r-4.5-any)
Saqrmisc_0.9.8.tar.gz(r-4.7-any)Saqrmisc_0.9.8.tar.gz(r-4.6-any)
Saqrmisc_0.9.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Saqrmisc/json (API)

# Install 'Saqrmisc' in R:

install.packages('Saqrmisc', repos = c('https://mohsaqr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mohsaqr/saqrmisc/issues

On CRAN:

3.60 score 6 scripts 46 exports 184 dependencies

Last updated from:364f9cfe6e. Checks:4 ERROR, 2 OK, 3 NOTE. Indexed: yes.

Target	Result	Time
linux-devel	ERROR	313
source / vignettes	OK	567
linux-release	NOTE	296
macos-release	ERROR	189
macos-oldrel	ERROR	175
windows-devel	ERROR	195
windows-release	NOTE	195
windows-oldrel	NOTE	188
wasm-release	OK	196

Exports:add_total auto_describe categorical_table center center_vec clustering clustering_plot_types compare_groups compare_networks convert_sequences correlation_matrix correlations counts data_overview descriptive_table estimate_grouped_networks estimate_single_network is_outlier launch_app missing_analysis mosaic_analysis normality_check outlier_check pass plot_clustering replace_missing replace_outliers reverse_code reverse_code_vec saqr_result scale_vars scale_vec set_api_key set_claude_key set_gemini_key set_openai_key set_openrouter_key standardize standardize_vec to_dataframe to_gt to_html to_kable to_latex to_markdown winsorize_vec

Dependencies:abind backports base64enc BayesFactor bayestestR bigD bit bit64 bitops boot bootnet broom bslib cachem checkmate class cli clipr cluster cocor coda codetools colorspace commonmark contfrac corpcor corrplot cpp11 crayon curl data.table datawizard deSolve digest doParallel dplyr e1071 effectsize effsize eigenmodel ellipse elliptic evaluate farver fastmap fdrtool fontawesome forcats foreach foreign Formula fs gdata generics ggplot2 glasso glassoFast glmnet glue GPArotation gridExtra gt gtable gtools haven highr Hmisc hms htmlTable htmltools htmlwidgets hypergeo igraph insight IsingFit IsingSampler isoband iterators jomo jpeg jquerylib jsonlite juicyjuice knitr labeling latents lattice lavaan lifecycle litedown lme4 lmtest magrittr mantar markdown MASS mathjaxr Matrix MatrixModels memoise mgm mice mime minqa mitml mnormt mvtnorm NetworkToolbox networktools nlme nloptr nnet nnls numDeriv ordinal pan parameters pbapply pbivnorm performance pillar pkgconfig plotrix plyr png polynom ppcor prettyunits progress proxy psych purrr pwr qgraph quadprog R.matlab R.methodsS3 R.oo R.utils R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reactable reactR readr reformulas reshape2 rlang rmarkdown rpart rstudioapi S7 sass scales shape smacof snow stringi stringr survival tibble tidyr tidyselect tinytex tzdb ucminf utf8 V8 vcd vctrs viridisLite vroom weights withr wordcloud xfun xml2 yaml zoo

Introduction to Saqrmisc: Comprehensive Data Analysis and Visualization

Rendered fromSaqrmisc-introduction.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2025-07-26

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add Row Totals to Data Frame	add_total
Tidy accessor for a mosaic_analysis result	as.data.frame.mosaic_analysis
Automatically Generate Descriptive Statistics for All Variables	auto_describe
Generate Publication-Ready Frequency Tables for Categorical Variables	categorical_table
Center Variables (Mean-Centering)	center
Center a Vector (for use in mutate/across)	center_vec
Latent Profile Clustering	clustering
List the Plot Types of plot_clustering()	clustering_plot_types
Generate Group Comparison Plots and Statistics	compare_groups
Compare Two Networks	compare_networks
Convert Sequence Data to Various Formats	convert_sequences
Create a Publication-Ready Correlation Matrix	correlation_matrix
Full Pairwise Correlation Table	correlations
Count Events per ID	counts
Quick Dataset Overview	data_overview
Generate Publication-Ready Descriptive Statistics Table	descriptive_table
Estimate Grouped Networks	estimate_grouped_networks
Estimate a Single Network	estimate_single_network
Input Data with Profile Assignments	fitted.saqr_clustering
Check if Value is Outlier (vectorized for mutate/across)	is_outlier
Launch the Saqrmisc mosaic analysis Shiny app	launch_app
Analyze Missing Data Patterns	missing_analysis
Perform a comprehensive mosaic plot analysis	mosaic_analysis
Check Normality of Variables	normality_check
Check for Outliers	outlier_check
Pass R Output to AI for Interpretation	pass
Plot a Clustering Result	plot_clustering
Plot method for mosaic_analysis objects	plot.mosaic_analysis
Plot Method for Clustering Results	plot.saqr_clustering
Print method for grouped_network_analysis objects	print.grouped_network_analysis
Print method for html tables	print.html_table
Print method for latex tables	print.latex_table
Print method for markdown tables	print.markdown_table
Print method for mosaic_analysis objects	print.mosaic_analysis
Print method for stratified mosaic_analysis objects	print.mosaic_stratified
Print method for network_analysis objects	print.network_analysis
Print a Clustering Result	print.saqr_clustering
Print Several Clustering Plots	print.saqr_plots
Print method for saqr_result	print.saqr_result
Replace Missing Values	replace_missing
Replace Outliers	replace_outliers
Reverse Code Variables	reverse_code
Reverse Code a Vector (for use in mutate/across)	reverse_code_vec
Create a saqr_result object	saqr_result
Scale Variables	scale_vars
Scale a Vector (for use in mutate/across)	scale_vec
Set API Key and Options for Session	set_api_key
Set Anthropic (Claude) API Key	set_claude_key
Set Google Gemini API Key	set_gemini_key
Set OpenAI API Key	set_openai_key
Set OpenRouter API Key	set_openrouter_key
Standardize Variables (Z-Score)	standardize
Standardize a Vector (for use in mutate/across)	standardize_vec
Summary method for mosaic_analysis objects	summary.mosaic_analysis
Summary method for stratified mosaic_analysis objects	summary.mosaic_stratified
Summary method for network_analysis objects	summary.network_analysis
Compare the Candidate Models of a Clustering Result	summary.saqr_clustering
Convert Any Table to Data Frame	to_dataframe
Convert Table to GT Format	to_gt
Convert Table to HTML Format	to_html
Convert Table to Kable Format	to_kable
Convert Table to LaTeX Format	to_latex
Convert Table to Markdown Format	to_markdown
Winsorize a Vector (vectorized for mutate/across)	winsorize_vec