Minor changes to theme = 1
in pirateplot()
. Changed default value of cap.beans
to TRUE
Added color mixing arguments mix.col
and mix.p
to piratepal()
. These allow you to mix the default palettes with a specified color (e.g.; "white"
)
Added the option to specify data in pirateplot()
as a list of numeric vectors, or as a numeric dataframe or matrix without specifying a formula. Each column / element will be taken as a new group.
New palettes in piratepal()
: decision
.
Fixed bug in sortx
in pirateplot()
. Sorting data by functions (e.g. sortx = "mean"
) should now work.
Added gl
argument to pirateplot()
to specify locations of gridlines (e.g.; gl = seq(0, 10, 1)
)
Added cex.names
argument to control size of bean names (currently this was controlled by cex.lab
, which now controls the size of the axis names.)
Some minor changes to default plotting parameters that I think make the default plots look a bit nicer.
Added cap.beans
argument to pirateplot()
. When cap.beans = TRUE
, beans will be cut at the maximum and minimum values of the data.
Added cap.beans
argument to pirateplot()
. When cap.beans = TRUE
, beans will be cut at the maximum and minimum values of the data.
Added two new inf.method
values: sd
for standard deviation, and se
for standard error
Minor updates to themes. Added theme = 3
You can now assign a pirateplot to a variable to return summary statistics.
Added a NEWS.md
file to track changes to the package.
Re-structured code generating colors and opacities in pirateplot()
to make future updates easier.
Added quant
, quant.length
and quant.width
arguments that add horizontal lines for specified quantiles to each bean (thanks @pat-s)
Added several new arguments (e.g.; bean.fill.col
for customising pirateplots
Improved theme support (now under theme
rather than theme.o
)
pirateplot()
can now handle up to 3 IVs!
Example: pirateplot(age ~ sex + headband + favorite.pirate, data = pirates)
.
Levels of the third IV are shown in separate plots in a grid.Minor and Bug-fixes
inf.p
parameter in pirateplot()
was prevously not being passed to the Bayesian HDIs, rendering all inference bands to be the default of 95% (thanks to Roman Pahl for catching this). This has now been fixed.hdi.band
argument to pirateplot()
. Setting hdi.band = "tight"
will constrain inference bands to bean densities.gl.col
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