fitPS

fitPS fits zeta-distribution models to forensic survey data, especially count data from clothing surveys involving glass or paint transfer.

The package supports two related survey data types:

• P data: counts of the number of groups or sources found on clothing.
• S data: counts of group sizes.

The main workflow is:

1. Create or read a psData object.
2. Fit a model with fitDist() or fitZIDist().
3. Inspect, predict, or compare fitted psFit objects.

Installation

Install from the repository root during development:

devtools::install()

or from GitHub when the repository is available:

remotes::install_github("jmcurran/fitPS")

Data format

Input files for readData() must contain exactly two columns:

• one column named P or S;
• one column named count.

For P data, the P column contains counts such as 0, 1, 2, and so on. For S data, the S column contains group sizes such as 1, 2, 3, and so on.

Example CSV:

P,count
0,98
1,1
2,1

Creating data manually

library(fitPS)

pData = makePSData(
  n = c(0, 1, 2),
  count = c(98, 1, 1),
  type = "P"
)

pData

For S data:

sData = makePSData(
  n = 1:3,
  count = c(1, 1, 1),
  type = "S"
)

sData

Reading data from a file

pData = readData(system.file("extdata", "p.xlsx", package = "fitPS"))
sData = readData(system.file("extdata", "s.xlsx", package = "fitPS"))

CSV files with the same two-column layout can also be read:

pData = readData("survey.csv")

Zeta shape parameterisation

fitPS uses shape for the zeta distribution shape parameter, with shape > 1. Users should supply, inspect, compare, and report shape on this scale.

Fitting a zeta model

fit = fitDist(pData)
fit

fitDist() returns a psFit object. Standard methods include printing, plotting, prediction, fitted values, confidence intervals, and log-likelihood extraction.

logLik(fit)
predict(fit)

Fitting a zero-inflated zeta model

ziFit = fitZIDist(pData)
ziFit
predict(ziFit)

Probability functions

Use probfun() to create a function for computing fitted P or S probabilities.

pFun = probfun(fit)
pFun(0:5)

Comparing surveys

The package includes survey-comparison helpers:

compareSurveys(fit1, fit2)
compareSurveysLRT(data1, data2)

See the function documentation and vignettes for details.

Vignettes

The vignettes/ directory contains worked examples for simple fitting and confidence-region workflows. During staged package checks, built vignette artifacts are excluded so strict checks remain focused on package code, documentation, and tests.

Development checks

The stage 1 stabilization work added baseline testthat coverage and strict package checks. During development, use:

devtools::document()
devtools::test(stop_on_failure = TRUE, stop_on_warning = TRUE)
devtools::check(
  build_args = c("--no-build-vignettes"),
  args = c("--no-manual", "--ignore-vignettes", "--no-tests"),
  error_on = "note"
)

License

GPL (>= 2).