pyFIES

Python implementation of FAO's Food Insecurity Experience Scale (FIES) — the methodology behind UN SDG indicator 2.1.2 (prevalence of moderate-or-severe and severe food insecurity in the population).

pyfies is a from-scratch port of the R package RM.weights (Cafiero, Viviani, Nord), implementing the weighted Conditional Maximum Likelihood (CML) Rasch estimator, equating to FAO's 2014–2016 global standard, and probabilistic prevalence assignment along the latent food-insecurity trait.

Status: alpha. v0.1 covers the dichotomous Rasch model, equating, and prevalence estimation. The polytomous (partial credit) extension and full diagnostics suite are planned for v0.2.

Installation

pip install pyfies          # from PyPI
pip install -e ".[dev]"     # editable install with dev tooling

Requires Python 3.11+.

Quickstart

import pandas as pd
from pyfies import RaschModel, FAO_2014_2016, DEFAULT_FIES_ITEMS

# df has 8 columns named WORRIED, HEALTHY, FEWFOOD, SKIPPED,
# ATELESS, RUNOUT, HUNGRY, WHLDAY (1=affirmative, 0=negative, NA=missing)
X = df[DEFAULT_FIES_ITEMS].to_numpy()
w = df["sampling_weight"].to_numpy()

model = RaschModel().fit(X, sample_weight=w)
model.equate(reference=FAO_2014_2016)
result = model.prevalence()

print(f"Moderate or severe food insecurity: {result.moderate_or_severe:.1%}")
print(f"Severe food insecurity:             {result.severe:.1%}")

What it computes

1. Item severity parameters for each FIES question via weighted CML.
2. Person parameters per raw score (post-hoc MLE conditional on item parameters).
3. Equating to the FAO 2014–2016 global metric so prevalence rates are comparable across countries and survey rounds.
4. Prevalence rates at any latent-trait threshold via Gaussian-mixture probabilistic assignment.

Methodology references

• FAO. The Food Insecurity Experience Scale — Development of a Global Standard for Monitoring Hunger Worldwide. Technical Paper v1.1, 2016. link
• Cafiero, C., Viviani, S., Nord, M. (2018). Food security measurement in a global context: The Food Insecurity Experience Scale. Measurement, 116, 146–152. doi:10.1016/j.measurement.2017.10.065
• FAO. Voices of the Hungry / FIES landing page.

Numerical parity with RM.weights

pyFIES is validated against the reference R package on the data.FAO_country1..4 sample datasets shipped with RM.weights. To regenerate parity fixtures (R required, one-time):

Rscript scripts/generate_r_fixtures.R

Tolerances: atol=2e-4 on item severities, atol=5e-3 on prevalence rates (typically achieved within 0.3 percentage points). See Parity for details.

Citing

If you use pyFIES in published research, please cite both the package and the underlying FAO methodology. The package's "Cite this repository" button on GitHub (powered by CITATION.cff) will give you the package citation in BibTeX, APA, and other formats. The Zenodo Concept DOI

10.5281/zenodo.19838795

always resolves to the latest release. To pin to a specific version, use the version-specific DOI shown on the Zenodo record page.

For the underlying methodology, cite Cafiero, C., Viviani, S., & Nord, M. (2018). Food security measurement in a global context: The Food Insecurity Experience Scale. Measurement, 116, 146–152. doi:10.1016/j.measurement.2017.10.065.

License

Apache 2.0.