ungroup.partition

Ungroups the tree object in the output from partition()

In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects
to gain evidence of placing a suspect at the crime scene.
This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the
person and at the crime scene, and assessing the significance in a likeness that they share.
The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and
James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) <doi:10.1016/S0379-0738(96)02037-3>
"The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14)
for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples.
It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples
with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2
distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997)
<doi:10.1016/S1355-0306(97)72197-X> "The interpretation of elemental composition measurements from forensic
glass evidence", Science & Justice, 37(4), 241--244).

Toby Hayward

SK4FGA

Scott-Knott for Forensic Glass Analysis

James Curran

Lewis Kendall-Jones

ungroup.partition function

<dl><dt>tree</dt>
<dd>tree object returned from partition()</dd></dl>

Arguments

ungroup.partition — ungroup.partition

<dl>

<dt>tree</dt>
<dd>tree object returned from partition()</dd>

</dl>

ungroup.partition: ungroup.partition

Description

Usage

Value

Arguments