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SK4FGA  

Scott-Knott for Forensic Glass Analysis
View on CRAN: Click here


Download and install SK4FGA package within the R console
Install from CRAN:
install.packages("SK4FGA")

Install from Github:
library("remotes")
install_github("cran/SK4FGA")

Install by package version:
library("remotes")
install_version("SK4FGA", "0.1.1")



Attach the package and use:
library("SK4FGA")
Maintained by
Toby Hayward
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-01-25
Latest Update: 2023-01-30
Description:
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).
How to cite:
Toby Hayward (2023). SK4FGA: Scott-Knott for Forensic Glass Analysis. R package version 0.1.1, https://cran.r-project.org/web/packages/SK4FGA. Accessed 30 Jan. 2025.
Previous versions and publish date:
0.1.0 (2023-01-25 11:00)
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