This function is essentially hdr.boxplot
but it more
easily works with matrices of data, where each column is a different variable
of interest. It has some limitations though....
1 2 3 4 5 
dat 
a matrix of data for which density region boxplots will be constructed and plotted for each column. 
probs 
a vector of credible intervals to represent as box edges.
Defaults to 
xlab 
a string for the xaxis label. Defaults to 
ylab 
a string fo the yaxis label. Defaults to 
xticklabels 
a vector of strings to override the xaxis tick labels. 
yticklabels 
a vector of strings to override the yaxis tick labels. 
clr 
a vector of colours to use for shading each of the box regions.
Defaults to greyscale 
scl 
a scalar multiplier to scale the box widths. Defaults to 1. 
xspc 
a scalar determining the amount of spacing between each box. Defaults to 0.5. 
prn 
a logical value determining whether summary statisics of each
column should be printed to screen 
ct 
a string of either 
ylims 
a vector of length two, specifying the lower and upper limits for the yaxis. Defaults to NULL which inspects the data for appropriate limits. 
lbound 
a lower boundary to specify on the distribution to avoid the
density kernel estimating values beyond that which can be expecte a priori.
Useful for example when plotting dietary proportions which must lie in the
interval 
ubound 
an upper boundary to specify on the distribution to avoid the
density kernel estimating values beyond that which can be expecte a priori.
Useful for example when plotting dietary proportions which must lie in the
interval 
main 
a title for the figure. Defaults to blank. 
ylab.line 
a postive scalar indicating the line spacing for rendering
the yaxis label. This is included as using the permille symbol has a
tendancy to push the axis label off the plotting window margins. See the

... 
further graphical parameters for passing to

A new figure window.
: This function will not currently recognise and plot
multimodal distributions, unlike hdr.boxplot
. You
should take care, and plot basic histograms of each variable (column in the
object you are passing) to siardensityplot
and check that they are
indeed unimodal as expected.
1 2  Y < matrix(stats::rnorm(1000), 250, 4)
siberDensityPlot(Y)

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