CITATION.cff

cff-version: 1.2.0
message: "If you use pyFIES in published research, please cite it as below, and also cite the underlying FIES methodology (Cafiero, Viviani, & Nord, 2018; doi:10.1016/j.measurement.2017.10.065)."
type: software
title: "pyFIES: Python implementation of FAO's Food Insecurity Experience Scale"
abstract: >-
  pyFIES is a Python package for computing UN SDG indicator 2.1.2 (the prevalence of
  moderate-or-severe and severe food insecurity) from raw survey responses. It
  implements FAO's official methodology: a weighted Conditional Maximum Likelihood
  Rasch model, equating to the FAO 2014–2016 global standard, and probabilistic
  prevalence assignment along the latent food-insecurity trait. Numerical results
  are validated against the R reference implementation (RM.weights).
authors:
  - family-names: Johnson
    given-names: Nicholas E.
    email: nicholas.e.johnson@stonybrook.edu
    affiliation: Stony Brook University
repository-code: "https://github.com/nejohnson2/pyFIES"
url: "https://nejohnson2.github.io/pyFIES/"
license: Apache-2.0
version: 0.1.1
date-released: "2026-04-27"
identifiers:
  - type: doi
    value: 10.5281/zenodo.19838795
    description: Concept DOI (resolves to the latest archived release)
keywords:
  - food insecurity
  - food security measurement
  - FIES
  - SDG 2.1.2
  - Rasch model
  - item response theory
  - psychometrics
  - survey analysis
  - FAO
references:
  - type: article
    authors:
      - family-names: Cafiero
        given-names: Carlo
      - family-names: Viviani
        given-names: Sara
      - family-names: Nord
        given-names: Mark
    title: "Food security measurement in a global context: The Food Insecurity Experience Scale"
    journal: Measurement
    volume: 116
    start: 146
    end: 152
    year: 2018
    doi: 10.1016/j.measurement.2017.10.065