Package: STATassist 1.1.0

Wonseok Oh

STATassist: Standardised Statistical Comparison Workflows

Runs the applicable tests for a comparison in one call and returns standardised result tables. One-sample, two-group, multi-group, factorial and categorical workflows report parametric, rank-based and robust results side by side with effect sizes, confidence intervals and multiplicity-adjusted p-values. Supervised fits, embeddings, clustering and simulators follow the same result contracts. Methods include those of Welch (1947) <doi:10.1093/biomet/34.1-2.28>, Wilcoxon (1945) <doi:10.2307/3001968>, Mann and Whitney (1947) <doi:10.1214/aoms/1177730491>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Friedman (1937) <doi:10.1080/01621459.1937.10503522>, Tukey (1949) <doi:10.2307/3001913>, Dunn (1964) <doi:10.1080/00401706.1964.10490181>, Yuen (1974) <doi:10.1093/biomet/61.1.165>, Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>, Algina, Keselman and Penfield (2005) <doi:10.1037/1082-989X.10.3.317>, DeLong, DeLong and Clarke-Pearson (1988) <doi:10.2307/2531595>, Sun and Xu (2014) <doi:10.1109/LSP.2014.2337313>, Pencina, D'Agostino, D'Agostino and Vasan (2008) <doi:10.1002/sim.2929>, and Pencina, D'Agostino and Steyerberg (2011) <doi:10.1002/sim.4085>.

Authors:Wonseok Oh [aut, cre]

STATassist_1.1.0.tar.gz
STATassist_1.1.0.zip(r-4.7-any)STATassist_1.1.0.zip(r-4.6-any)STATassist_1.1.0.zip(r-4.5-any)
STATassist_1.1.0.tgz(r-4.6-any)STATassist_1.1.0.tgz(r-4.5-any)
STATassist_1.1.0.tar.gz(r-4.7-any)STATassist_1.1.0.tar.gz(r-4.6-any)
STATassist_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
STATassist/json (API)

# Install 'STATassist' in R:

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

Bug tracker:https://github.com/hiows/statassist/issues

On CRAN:

3.88 score 3 stars 394 downloads 48 exports 91 dependencies

Last updated from:a1a7ef09e4. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	228
source / vignettes	OK	258
linux-release	OK	195
macos-release	OK	195
macos-oldrel	OK	174
windows-devel	OK	199
windows-release	OK	207
windows-oldrel	OK	202
wasm-release	OK	110

Exports:center_by_control cluster_dbscan cluster_hclust cluster_kmeans cluster_snn compare_categorical_groups compare_factorial_groups compare_multiple_groups compare_one_sample compare_two_groups diagnose_distribution draw_butterfly_hist draw_corrplot draw_dim_reduction_plot draw_forest_plot draw_grouped_barplot draw_grouped_boxplot draw_heatmap draw_interaction_plot draw_mosaic_plot draw_prediction_plot draw_roc_curve draw_volcano_plot estimate_categorical_significance estimate_significance evaluate_classification_models evaluate_regression_models fit_elastic_net fit_linear_regression fit_logistic_regression fit_rf fit_svm make_block_cor perform_pca perform_rfe perform_stepwise perform_tsne perform_umap screen_outliers simulate_categorical_groups simulate_classification simulate_factorial_groups simulate_multiple_groups simulate_regression simulate_two_groups split_data summarize_association_stats summarize_descriptive_stats

Dependencies:askpass caret class cli clock codetools cpp11 data.table dbscan diagram digest dplyr e1071 farver foreach future future.apply generics ggplot2 glmnet globals glue gower gtable hardhat here ipred isoband iterators jsonlite kernlab KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv openssl parallelly pillar pkgconfig plyr png pROC prodlim progressr proxy purrr R6 randomForest rappdirs RColorBrewer Rcpp RcppEigen RcppTOML recipes reshape2 reticulate rlang rpart rprojroot RSpectra Rtsne S7 scales shape sparsevctrs SQUAREM stringi stringr survival sys tibble tidyr tidyselect timechange timeDate tzdb umap utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
The contingency table a categorical comparison was run on	as.table.sa_categorical
Centre every feature on the control group	center_by_control
Cluster by finding the dense regions	cluster_dbscan
Cluster by building a tree and cutting it	cluster_hclust
Cluster by moving centres until they stop	cluster_kmeans
Cluster by how many neighbours points have in common	cluster_snn
Coefficients, summary and predictions from the model inside a fit	coef.sa_fit predict.sa_fit summary.sa_fit
Coefficients of a fitted model	coef.sa_model
Test a contingency table with every applicable test at once	compare_categorical_groups
Analyse a crossed-factor design as one model	compare_factorial_groups
Run every applicable multi-group test at once	compare_multiple_groups
Compare one sample against a hypothesised value	compare_one_sample
Run every applicable two-group test at once	compare_two_groups
Check the assumptions a comparison rests on	diagnose_distribution
Draw a butterfly histogram of one feature across two groups	draw_butterfly_hist
Draw a correlation matrix, with the cells that failed the test left blank	draw_corrplot
Draw a reduction as a scatter of its points	draw_dim_reduction_plot plot.sa_reduction
Draw a forest plot of a comparison result	draw_forest_plot plot.sa_comparison
Draw a grouped barplot of a descriptive summary	draw_grouped_barplot
Draw a grouped boxplot across several features	draw_grouped_boxplot
Draw a clustered heatmap of features by samples	draw_heatmap
Draw an interaction plot of a factorial comparison	draw_interaction_plot
Draw a mosaic plot of a contingency table	draw_mosaic_plot plot.sa_categorical
Draw predicted against observed for an evaluated regression	draw_prediction_plot
Draw the ROC curves of an evaluated classification	draw_roc_curve
Draw a volcano plot	draw_volcano_plot
Reduce a contingency table to significance verdicts	estimate_categorical_significance
Reduce a comparison to one significance verdict per feature	estimate_significance
Score fitted classifications on held-out rows	evaluate_classification_models
Score fitted regressions on held-out rows	evaluate_regression_models
Fit an elastic net, lasso or ridge regression	fit_elastic_net
Fit a linear regression	fit_linear_regression
Fit a logistic regression	fit_logistic_regression
Fit a random forest	fit_rf
Fit a support vector machine	fit_svm
Build a block correlation matrix	make_block_cor
Reduce many features to a few components	perform_pca
Select the predictors worth keeping	perform_rfe
Stepwise feature selection by information criteria	perform_stepwise
Embed samples or features with t-SNE	perform_tsne
Embed samples or features with UMAP	perform_umap
Draw an evaluation result	plot.sa_performance
Predict from a fitted model on rows it was not fitted to	predict.sa_model
Print a categorical comparison	print.sa_categorical
Print a categorical significance verdict	print.sa_categorical_significance
Print a clustering	print.sa_cluster
Print a comparison result	print.sa_comparison
Print a distribution diagnosis	print.sa_diagnosis
Print a fitted model	print.sa_model
Print an evaluation result	print.sa_performance
Print a dimensionality reduction	print.sa_reduction
Print a feature selection	print.sa_selection
Print a significance verdict	print.sa_significance
Print a train/test split	print.sa_split
Flag candidate outliers without removing them	screen_outliers
Simulate a contingency table whose association is known	simulate_categorical_groups
Simulate a two-class outcome whose coefficients are known	simulate_classification
Simulate a crossed-factor experiment whose answer is known	simulate_factorial_groups
Simulate a control-versus-treatments experiment whose answer is known	simulate_multiple_groups
Simulate a regression whose coefficients are known	simulate_regression
Simulate a two-group experiment whose answer is known	simulate_two_groups
Split data into training and test sets	split_data
Correlation between every pair of features, with all three coefficients	summarize_association_stats
Descriptive summary of several features, optionally split by group	summarize_descriptive_stats