Reference

scagnostics and supporting functions These are the primary functions for computing scagnostics
sc_clumpy()	Compute clumpy scagnostic measure using MST
sc_clumpy2()	Compute adjusted clumpy measure using MST
sc_clumpy_r()	Compute robust clumpy scagnostic measure using MST
sc_convex()	Compute convex scagnostic measure
sc_dcor()	Distance correlation index.
sc_monotonic()	Measure of Spearman Correlation
sc_outlying()	Compute outlying scagnostic measure using MST
sc_skewed()	Compute skewed scagnostic measure using MST
sc_skinny()	Compute skinny scagnostic measure
sc_sparse()	Compute sparse scagnostic measure using MST
sc_sparse2()	Compute adjusted sparse measure using the alpha hull
sc_splines()	Spline based index.
sc_striated()	Compute striated scagnostic measure using MST
sc_striated2()	Compute angle adjusted striated measure using MST
sc_stringy()	Compute stringy scagnostic measure using MST
sc_striped()	Measure of Discreteness
scree()	Pre-processing to generate scagnostic measures
drawing functions Draw building blocks of the scagnostics
draw_alphahull()	Drawing the alphahull
draw_convexhull()	Drawing the Convex Hull
draw_mst()	Drawing the MST
summary functions Functions to compute multiple scagnostics and summarise output
calc_scags()	Compute selected scagnostics on subsets
calc_scags_wide()	Compute scagnostics on all possible scatter plots for the given data
top_pairs()	Calculate the top scagnostic for each pair of variables
top_scags()	Calculate the top pair of variables or group for each scagnostic
data Sample data sets for illustration and testing
numbat	A toy data set with a numbat shape hidden among noise variables
anscombe_tidy	Data from Anscombe's famous example in tidy format
datasaurus_dozen datasaurus_dozen_wide	datasaurus_dozen data
features	Simulated data with special features
pk	Parkinsons data from UCI machine learning archive