Package: SK4FGA 0.1.1

SK4FGA: Scott-Knott for Forensic Glass Analysis

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).

Authors:Toby Hayward [aut, cre], James Curran [aut, ctb], Lewis Kendall-Jones [ctb]

SK4FGA_0.1.1.tar.gz
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SK4FGA_0.1.1.tgz(r-4.4-x86_64)SK4FGA_0.1.1.tgz(r-4.4-arm64)SK4FGA_0.1.1.tgz(r-4.3-x86_64)SK4FGA_0.1.1.tgz(r-4.3-arm64)
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SK4FGA.pdf |SK4FGA.html
SK4FGA/json (API)

# Install 'SK4FGA' in R:
install.packages('SK4FGA', repos = c('https://tobyhayward13.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/tobyhayward13/sci118uoa_forensicglassanalysis/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

2.70 score 152 downloads 11 exports 1 dependencies

Last updated 2 years agofrom:213c3e9497. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-win-x86_64NOTENov 07 2024
R-4.5-linux-x86_64NOTENov 07 2024
R-4.4-win-x86_64NOTENov 07 2024
R-4.4-mac-x86_64NOTENov 07 2024
R-4.4-mac-aarch64NOTENov 07 2024
R-4.3-win-x86_64OKNov 07 2024
R-4.3-mac-x86_64OKNov 07 2024
R-4.3-mac-aarch64OKNov 07 2024

Exports:calculate_lambda_thresholdfind_B0find_T0find_T2generate_indicespartitionpartition.multiplot.sk_partition_treeprepare_dataptsquaredungroup.partition

Dependencies:Rcpp