Functions for Pre-Processing Data for Multivariate Data Visualisation using Tours

Documentation for package ‘mulgar’ version 1.0.2

aflw	AFLW player statistics
box	3D plane in 5D
bushfires	Australian bushfires 2019-2020
c1	Challenge data sets
c2	Challenge data sets
c3	Challenge data sets
c4	Challenge data sets
c5	Challenge data sets
c6	Challenge data sets
c7	Challenge data sets
calc_mv_dist	Compute Mahalanobis distances between all pairs of observations
calc_norm	Calculate the norm of a vector
clusters	Three clusters in 5D
clusters_nonlin	Four unusually shaped clusters in 4D
convert_proj_tibble	This function turns a projection sequence into a tibble
gen_vc_ellipse	Generate points on the surface of an ellipse
gen_xvar_ellipse	Ellipse matching data center and variance
ggmcbic	Produces an mclust summary plot with ggplot
ggscree	This function produces a simple scree plot
ggslice	Generate an axis-parallel slice display
ggslice_projection	Generate slice display
hierfly	Generate a dendrogram to be added to data
mc_ellipse	Computes the ellipses of an mclust model
multicluster	Multiple clusters of different sizes, shapes and distance from each other
norm_vec	Normalise a vector to have length 1
pca_model	Create wire frame of PCA model
pisa	PISA scores
plane	2D plane in 5D
plane_nonlin	Non-linear relationship in 5D
pooled_vc	Compute pooled variance-covariance matrix
rmvn	Generate a sample from a multivariate normal
simple_clusters	Two clusters in 2D
sketches_test	Images of sketches for testing
sketches_train	Images of sketches for training
som_model	Process the output from SOM to display the map and data